Scopus
EXPORT DATE: 31 August 2012
Hu, M.a , Wu, T.a , Weir, J.D.b
An intelligent augmentation of particle swarm optimization with multiple adaptive methods
(2012) Information Sciences, 213, pp. 68-83.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863726256&partnerID=40&md5=ae8b7f6dcf18af8725aa6679f3edf0d3
AFFILIATIONS: School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ 85287-5906, United States;
Department of Operational Sciences, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433-7765, United States
ABSTRACT: Over the last two decades, the newly developed optimization technique - Particle Swarm Optimization (PSO) has attracted great attention. Two common criticisms exist. First, most existing PSOs are designed for a specific search space thus an algorithm performing well on a diverse set of problems is lacking. Secondly, PSO suffers premature convergence. To address the first issue, we propose to augment PSO via the fusion of multiple search methods. An intelligent selection mechanism is developed based on an effectiveness index to trigger appropriate search methods. In this research, two search techniques are studied: a non-uniform mutation-based method and an adaptive sub-gradient method. We further improve the proposed PSO using adaptive Cauchy mutation to prevent premature convergence. As a result, an augmented PSO with multiple adaptive methods (PSO-MAM) is proposed. The performance of PSO-MAM is tested on 43 functions (uni-modal, multi-modal, non-separable, shifted, rotated, noisy and mis-scaled functions). The results are compared in terms of solution quality and convergence speed with 10 published PSO methods. The experimental results demonstrate PSO-MAM outperforms the comparison algorithms on 36 out of 43 functions. We conclude, while promising, there is still room for improving PSO-MAM on complex multi-modal functions (e.g., rotated multi-modal functions). © 2012 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Adaptive multi-method; Cauchy mutation; Non-uniform mutation; Particle swarm optimization; Sub-gradient
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DOCUMENT TYPE: Article
SOURCE: Scopus
Nasir, M.a c , Das, S.b c c , Maity, D.a c c , Sengupta, S.a c c , Halder, U.a c c , Suganthan, P.N.b c c
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
(2012) Information Sciences, 209, pp. 16-36.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862688092&partnerID=40&md5=1b36513034502176096d25b15ae8f0b3
AFFILIATIONS: Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India;
Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700 108, India;
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
ABSTRACT: The concept of particle swarms originated from the simulation of the social behavior commonly observed in animal kingdom and evolved into a very simple but efficient technique for optimization in recent past. Since its advent in 1995, the Particle Swarm Optimization (PSO) algorithm has attracted the attention of a lot of researchers all over the world resulting into a huge number of variants of the basic algorithm as well as many parameter selection/control strategies. PSO relies on the learning strategy of the individuals to guide its search direction. Traditionally, each particle utilizes its historical best experience as well as the global best experience of the whole swarm through linear summation. The Comprehensive Learning PSO (CLPSO) was proposed as a powerful variant of PSO that enhances the diversity of the population by encouraging each particle to learn from different particles on different dimensions, in the metaphor that the best particle, despite having the highest fitness, does not always offer a better value in every dimension. This paper presents a variant of single-objective PSO called Dynamic Neighborhood Learning Particle Swarm Optimizer (DNLPSO), which uses learning strategy whereby all other particles' historical best information is used to update a particle's velocity as in CLPSO. But in contrast to CLPSO, in DNLPSO, the exemplar particle is selected from a neighborhood. This strategy enables the learner particle to learn from the historical information of its neighborhood or sometimes from that of its own. Moreover, the neighborhoods are made dynamic in nature i.e. they are reformed after certain intervals. This helps the diversity of the swarm to be preserved in order to discourage premature convergence. Experiments were conducted on 16 numerical benchmarks in 10, 30 and 50 dimensions, a set of five constrained benchmarks and also on a practical engineering optimization problem concerning the spread-spectrum radar poly-phase code design. The results demonstrate very competitive performance of DNLPSO while locating the global optimum on complicated and multimodal fitness landscapes when compared with five other recent variants of PSO. © 2012 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Exemplar; Learning strategy; Neighborhood; Particle swarm
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DOCUMENT TYPE: Article
SOURCE: Scopus
Gao, F.a b , Fei, F.-X.a , Deng, Y.-F.a , Qi, Y.-B.a , Ilangko, B.b c d
A novel non-Lyapunov approach through artificial bee colony algorithm for detecting unstable periodic orbits with high orders
(2012) Expert Systems with Applications, 39 (16), pp. 12389-12397.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864485904&partnerID=40&md5=2ea68fedf521d64f524fa2372d0e20cd
AFFILIATIONS: Department of Mathematics, School of Science, Wuhan University of Technology, Luoshi Road 122, Wuhan, Hubei 430070, China;
Signal Processing Group, Department of Electronics and Telecommunications, Norwegian University of Science and Technology, N-7491 Trondheim, Norway;
Intervention Center, Oslo University Hospital, 0424 Oslo, Norway;
Institute of Clinical Medicine, University of Oslo, 0316 Oslo, Norway
ABSTRACT: In this paper, a novel non-Lyapunov way is proposed to detect the unstable periodic orbits (UPOs) with high orders by a new artificial bee colony algorithm (ABC). And UPOs with high orders of nonlinear systems, are one of the most challenging problems of nonlinear science in both numerical computations and experimental measures. The proposed method maintains an effective searching mechanism with fine equilibrium between exploitation and exploration. To improve the performance for the optimums of the multi-model functions and to avoid the coincidences among the UPOs with different orders, we add the techniques as function stretching, deflecting and repulsion to ABC. The problems of detecting the UPOs are converted into a non-negative functions' minimization through a proper translation, which finds a UPO such that the objective function is minimized. Experiments to different high orders UPOs of 5 wellknown and widely used nonlinear maps indicate that the proposed algorithm is robust, by comparison of results through the ABC and quantum-behaved particle swarm optimization (QPSO), respectively. And it is effective even in cases where the Newton-family algorithms may not be applicable. Density of the orbits are discussed. Simulation results show that ABC is superior to QPSO, and it is a successful method in detecting the UPOs, with the advantages of fast convergence, high precision and robustness. © 2012 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Artificial bee colony algorithm; Non-Lyapunov; Non-negative functions' minimization; Unstable periodic orbits
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DOCUMENT TYPE: Review
SOURCE: Scopus
Alvarado, M., Rendón, A.Y.
Nash equilibrium for collective strategic reasoning
(2012) Expert Systems with Applications, 39 (15), pp. 12014-12025.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863108055&partnerID=40&md5=7b9e34c48214d6a72f4a880935120fd8
AFFILIATIONS: Computer Sciences Department, Center of Research and Advanced Studies, Av. Instituto Politécnico Nacional 2508, San Pedro Zacatenco, CP 07360 México DF, Mexico
ABSTRACT: In multi-player games, the Nash Equilibrium (NE) profile concept deserves a team for selecting strategies during a match, so no player - except in own prejudice - individually deviates from the team selected strategy. By using NE strategy profiles, the way a baseball team increases the possibilities to a match victory is payoff-matrices-based analyzed in this paper. Each matrix entry arrange each player's strategies by regarding the ones from mates and adversaries, and posterior to a NE-profile-selection, the matrix from all players strategies can support the manager's strategic decision-making in the course of a match. A finite state machine, a formal grammar and a generator of random plays are the algorithmic fundament for this collective strategic reasoning automation. The relationships to e-commerce, social and political scopes, as well as to computing issues are reviewed. © 2012 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Multi-player games; Nash equilibrium; Strategic reasoning; Team strategies
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SOURCE: Scopus
Boubezoul, A.a , Paris, S.b
Application of global optimization methods to model and feature selection
(2012) Pattern Recognition, 45 (10), pp. 3676-3686.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861820472&partnerID=40&md5=67e02766f2de21dbcaee518b3ef7c78d
AFFILIATIONS: Paris-Est University, IFSTTAR, LEPSIS, F-75732 Paris, France;
Laboratory of Sciences of Informations and of System, Aix-Marseille University, LSIS/DYNI UMR CNRS 7296, Av Escadrille de Normandie Niemen, 13397 Marseille Cedex 20, France
ABSTRACT: Many data mining applications involve the task of building a model for predictive classification. The goal of this model is to classify data instances into classes or categories of the same type. The use of variables not related to the classes can reduce the accuracy and reliability of classification or prediction model. Superfluous variables can also increase the costs of building a model particularly on large datasets. The feature selection and hyper-parameters optimization problem can be solved by either an exhaustive search over all parameter values or an optimization procedure that explores only a finite subset of the possible values. The objective of this research is to simultaneously optimize the hyper-parameters and feature subset without degrading the generalization performances of the induction algorithm. We present a global optimization approach based on the use of Cross-Entropy Method to solve this kind of problem. © 2012 Elsevier Ltd.
AUTHOR KEYWORDS: Cross-Entropy Method; Feature selection; Hyper-parameters optimization; Particle swarm optimization; Support vector machines
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DOCUMENT TYPE: Article
SOURCE: Scopus
Abedini, M.a , Vatankhah, R.a , Assadian, N.b
Stabilizing chaotic system on periodic orbits using multi-interval and modern optimal control strategies
(2012) Communications in Nonlinear Science and Numerical Simulation, 17 (10), pp. 3832-3842.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860321200&partnerID=40&md5=d93a7cf0b8135bcca75fcc3a75ad24f3
AFFILIATIONS: Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran;
Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran
ABSTRACT: In this paper, optimal approaches for controlling chaos is studied. The unstable periodic orbits (UPOs) of chaotic system are selected as desired trajectories, which the optimal control strategy should keep the system states on it. Classical gradient-based optimal control methods as well as modern optimization algorithm Particle Swarm Optimization (PSO) are utilized to force the chaotic system to follow the desired UPOs. For better performance, gradient-based is applied in multi-intervals and the results are promising. The Duffing system is selected for examining the proposed approaches. Multi-interval gradient-based approach can put the states on UPOs very fast and keep tracking UPOs with negligible control effort. The maximum control in PSO method is also low. However, due to its inherent random behavior, its control signal is oscillatory. © 2012 Elsevier B.V.
AUTHOR KEYWORDS: Chaos control; Classical gradient-based optimal control; Duffing system; Particle Swarm Optimization (PSO)
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Lazzouni, S.A., Bowong, S., Kakmeni, F.M.M., Cherki, B., Ghouali, N., Chaos control using small-amplitude damping signals of the extended Duffing equation (2007) Commun Nonlinear Sci Numer Simulat, 12, pp. 804-813;
Laskari, E.C., Parsopoulos, K.C., Vrahatis, M.N., Particle Swarm Optimization for integer programming (2002) Proc Evol Comput., 2, pp. 1582-1587;
Fourie, P.C., Groenwold, A., The particle swarm optimization algorithm in size and shape optimization (2002) Struct Multidisc, 23, pp. 259-267;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Papakostas, G.A., Koulouriotis, D.E., Polydoros, A.S., Tourassis, V.D.
Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems
(2012) Expert Systems with Applications, 39 (12), pp. 10620-10629.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861193823&partnerID=40&md5=e5f7e99fb368727af58b8178435e8d9e
AFFILIATIONS: Democritus University of Thrace, Department of Production Engineering and Management, 67100 Xanthi, Greece
ABSTRACT: A detailed comparative analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) operating as pattern classifiers, is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM classifier so it equilibrates to a desired state (class mapping). For these purposes, six different types of Hebbian learning algorithms from the literature have been selected and studied in this work. Along with the theoretical description of these algorithms and the analysis of their performance in classifying known patterns, a sensitivity analysis of the applied classification scheme, regarding some configuration parameters have taken place. It is worth noting that the algorithms are studied in a comparative fashion, under common configurations for several benchmark pattern classification datasets, by resulting to useful conclusions about their training capabilities. © 2012 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Classifier; Fuzzy Cognitive Maps; Hebbian learning; Pattern classification; Soft computing; Training
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Glykas, M., Fuzzy cognitive maps: Advances in theory, methodologies, tools and applications (2010) Studies in Fuzziness and Soft Computing, 247., Springer-Verlag;
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Li, S.J., Shen, R.M., Fuzzy cognitive map learning based on improved nonlinear Hebbian rule (2004) 3rd International Conference on Machine Learning and Cybernetics, pp. 2301-2306., 26-29 August, 2004 Shanghai;
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Papageorgiou, E.I., Groumpos, P.P., A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps (2005) Applied Soft Computing, 5 (4), pp. 409-431;
Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.S., Groumpos, P.P., Vrahatis, M.N., Fuzzy cognitive maps learning using particle swarm optimization (2005) Journal of Intelligent Information Systems, 25 (1), pp. 95-121;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Fabris, F., Krohling, R.A.
A co-evolutionary differential evolution algorithm for solving min-max optimization problems implemented on GPU using C-CUDA
(2012) Expert Systems with Applications, 39 (12), pp. 10324-10333.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861193423&partnerID=40&md5=749f2ca887eaf16a0e43caba36ed340b
AFFILIATIONS: Department of Computer Science, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, CEP 29075-910, Vitória, Espírito Santo, ES, Brazil
ABSTRACT: Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of graphics processing units (GPU) and the compute unified device architecture (CUDA) platform. In case of evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of a co-evolutionary differential evolution (DE) algorithm in C-CUDA for solving min-max problems. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced and scalability is improved using C-CUDA. As far as we know, this is the first implementation of a co-evolutionary DE algorithm in C-CUDA. © 2012 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Co-evolutionary algorithms; Computational performance assessment; Compute unified device architecture (CUDA); Differential evolution; Graphics processing unit (GPU); Optimization
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DOCUMENT TYPE: Article
SOURCE: Scopus
Askarzadeh, A., Rezazadeh, A.
An innovative global harmony search algorithm for parameter identification of a PEM fuel cell model
(2012) IEEE Transactions on Industrial Electronics, 59 (9), art. no. 6051479, pp. 3473-3480.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860153293&partnerID=40&md5=374f0a40cfa1c7bad21b678df61743e6
AFFILIATIONS: Faculty of Electrical and Computer Engineering, Shahid Beheshti University, Tehran 1983963113, Iran
ABSTRACT: Optimum modeling of proton exchange membrane (PEM) fuel cell has become the major focus of various researches. The main drawback in optimum modeling is that the model parameters are unknown, and empirical values are not sufficient to exactly model it. Since the characteristic of a PEM fuel cell is highly nonlinear, an excellent optimization technique is needed. In this paper, an innovative global harmony search (IGHS) algorithm-based parameter identification method is proposed. The IGHS algorithm is employed for parameter identification of the SR-12 Modular PEM Generator, the Ballard Mark V FC, and the BCS 500-W stack, and its performance is compared with that of two versions of harmony search algorithms, three versions of particle swarm optimization algorithms, bee swarm optimization algorithm, and seeker optimization algorithm. Simulation results reveal that the proposed technique gives both better and more robust results than the other studied algorithms. © 2012 IEEE.
AUTHOR KEYWORDS: Innovative global harmony search (IGHS); optimization algorithm; parameter identification; proton exchange membrane (PEM) fuel cell (FC)
REFERENCES: Ramos-Paja, C.A., Bordons, C., Romero, A., Giral, R., Martinez-Salamero, L., Minimum fuel consumption strategy for PEM fuel cells (2009) IEEE Trans. Ind. Electron., 56 (3), pp. 685-696., Mar;
Jemei, S., Hissel, D., Pera, M.C., Kauffmann, J.M., A new modeling approach of embedded fuel-cell power generators based on artificial neural network (2008) IEEE Trans. Ind. Electron., 55 (1), pp. 437-447., Jan;
Kong, X., Khambadkone, A.M., Modeling of a PEM fuel-cell stack for dynamic and steady-state operation using ANN-based submodels (2009) IEEE Trans. Ind. Electron., 56 (12), pp. 4903-4914., Dec;
Puranik, S.V., Keyhani, A., Khorrami, F., Neural network modeling of proton exchange membrane fuel cell (2010) IEEE Trans. Energy Convers., 25 (2), pp. 474-483., Jun;
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Corrêa, J.M., Farret, F.A., Canha, L.N., Simões, M.G., An electrochemical-based fuel-cell model suitable for electrical engineering automation approach (2004) IEEE Trans. Ind. Electron., 51 (5), pp. 1103-1112., Oct;
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Marignetti, F., Minutillo, M., Perna, A., Jannelli, E., Assessment of fuel cell performance under different air stoichiometries and fuel composition (2011) IEEE Trans. Ind. Electron., 58 (6), pp. 2420-2426., Jun;
Xue, X.D., Cheng, K.W.E., Sutanto, D., Unified mathematical modelling of steady-state and dynamic voltage-current characteristics for PEM fuel cells (2006) Electrochim Acta, 52 (3), pp. 1135-1144., Nov;
Mo, Z.J., Zhu, X.J., Wei, L.Y., Cao, G.Y., Parameter optimization for a PEMFC model with a hybrid genetic algorithm (2006) Int. J. Energy Res., 30 (8), pp. 585-597., Jun;
Ohenoja, M., Leiviskä, K., Identification of electrochemical model parameters in PEM fuel cells (2009) Proc. 2nd Int. Conf. POWERENG, pp. 363-368., Lisbon, Portugal, Mar;
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Ye, M., Wang, X., Xu, Y., Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization (2009) Int. J. Hydrogen Energy, 34 (2), pp. 981-989., Jan;
Li, Q., Chen, W., Wang, Y., Liu, S., Jia, J., Parameter identification for PEM fuel-cell mechanism model based on effective informed adaptive particle swarm optimization (2011) IEEE Trans. Ind. Electron., 58 (6), pp. 2410-2419., Jun;
Outeiro, M.T., Chibante, R., Carvalho, A.S., De Almeida, A.T., A new parameter extraction method for accurate modeling of PEM fuel cell (2009) Int. J. Energy Res., 33 (11), pp. 978-988., Sep;
Dai, C., Chen, W., Zhu, Y., Seeker optimization algorithm for digital IIR filter design (2010) IEEE Trans. Ind. Electron., 57 (5), pp. 1710-1718., May;
Geem, Z.W., Kim, J.H., Loganathan, G.V., A new heuristic optimization algorithm: Harmony search (2001) Simulation, 76 (2), pp. 60-68., Feb;
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Yadav, P., Kumar, R., Panda, S.K., Chang, C.S., An improved harmony search algorithm for optimal scheduling of the diesel generators in oil rig platforms (2011) Energy Convers. Manage., 52 (2), pp. 893-902., Feb;
Wang, L., Pan, Q.K., Tasgetiren, M.F., Minimizing the total flow time in a flow shop with blocking by using hybrid harmony search algorithm (2010) Expert Syst. Appl., 37 (12), pp. 7929-7936., Dec;
Zou, D., Gao, L., Li, S., Wu, J., Wang, X., A novel global harmony search algorithm for task assignment problem (2010) J. Syst. Softw., 83 (10), pp. 1678-1688., Oct;
Mahdavi, M., Fesanghary, M., Damangir, E., An improved harmony search algorithm for solving optimization problems (2007) Appl. Math. Comput., 188 (2), pp. 1567-1579., May;
Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., Panigrahi, B.K., Exploratory power of the harmony search algorithm: Analysis and improvements for global numerical optimization (2011) IEEE Trans. Syst., Man, Cybern. B, Cybern., 41 (1), pp. 89-106., Feb;
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Pan, Q.K., Suganthan, P.N., Tasgetiren, M.F., Liang, J.J., A self-adaptive global best harmony search algorithm for continuous optimization problems (2010) Appl. Math. Comput., 216 (3), pp. 830-848., Apr;
Khan, M., Iqbal, M., Modelling and analysis of electrochemical, thermal, and reactant flow dynamics for a PEM fuel cell system (2005) Fuel Cells, 5 (4), pp. 463-475., Dec;
Mann, R.F., Amphlett, J.C., Hooper, M.A.I., Jensen, H.M., Peppley, B.A., Roberge, P.R., Development and application of a generalised steadystate electrochemical model for a PEM fuel cell (2000) J. Power Sources, 86 (1-2), pp. 173-180., Mar;
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Akbari, R., Mohammadi, A., Ziarati, K., A novel bee swarm optimization algorithm for numerical function optimization (2010) Commun. Nonlinear Sci. Numer. Simul., 15 (10), pp. 3142-3155., Oct;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Dye, C.-Y.
A finite horizon deteriorating inventory model with two-phase pricing and time-varying demand and cost under trade credit financing using particle swarm optimization
(2012) Swarm and Evolutionary Computation, 5, pp. 37-53.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861528335&partnerID=40&md5=a8190bebecade5871c27afce21edef1c
AFFILIATIONS: Graduate School of Business and Administration, Shu-Te University, Yen Chao, Kaohsiung 824, Taiwan
ABSTRACT: In this paper, we consider a deterministic economic order quantity model with generalized type demand, deterioration and unit purchase cost functions under two levels of trade credit policy. Our objective is to find the optimal values of selling prices, replenishment number and replenishment scheme which maximize the total profit over the finite planning horizon. We establish the inventory system and provide structural properties of the optimal solution that facilitate computation. A particle swarm optimization with constriction factor is coded and used to solve the mixed-integer nonlinear programming problem by employing the properties derived from this paper. At the end, some numerical examples are used to illustrate the features of the proposed model. © 2012 Elsevier B.V. All rights reserved.
AUTHOR KEYWORDS: Inventory; Particle swarm optimization; Time-varying demand; Trade credit financing
REFERENCES: Lee, H.L., Padmanabhan, V., Taylor, T.A., Whang, S., Price protection in the personal computer industry (2000) Management Science, 46, pp. 466-482;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Liu, Y., Li, W., Ma, R.
Particle swarm optimization on flexible docking
(2012) International Journal of Biomathematics, 5 (5), art. no. 1250044, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862580338&partnerID=40&md5=b5b4b1ce7978cf36ede7be47c88322d6
AFFILIATIONS: School of Software, Dalian University of Technology, Dalian 116024, China
ABSTRACT: Molecular docking is an important tool in screening large libraries of compounds to determine the interactions between potential drugs and the target proteins. The molecular docking problem is how to locate a good conformation to dock a ligand to the large molecule. It can be formulated as a parameter optimization problem consisting of a scoring function and a global optimization method. Many docking methods have been developed with primarily these two parts varying. In this paper, a variety of particle swarm optimization (PSO) variants were introduced to cooperate with the semiempirical free energy force field in AutoDock 4.05. The search ability and the docking accuracy of these methods were evaluated by multiple redocking experiments. The results demonstrate that PSOs were more suitable than Lamarckian genetic algorithm (LGA). Among all of the PSO variants, FIPS takes precedence over others. Compared with the four state-of-art docking methods-GOLD, DOCK, FlexX and AutoDock with LGA, AutoDock cooperated with FIPS is more accurate. Thus, FIPS is an efficient PSO variant which has promising prospects that can be expected in the application to virtual screening. © 2012 World Scientific Publishing Company.
AUTHOR KEYWORDS: AutoDock; molecular docking; particle swarm optimization
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DOCUMENT TYPE: Article
SOURCE: Scopus
Viral, R., Khatod, D.K.
Optimal planning of distributed generation systems in distribution system: A review
(2012) Renewable and Sustainable Energy Reviews, 16 (7), pp. 5146-5165.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862740308&partnerID=40&md5=ad08af9681fdfa8f78bafd45e21d3bc7
AFFILIATIONS: Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
ABSTRACT: This paper attempts to present the state of art of research work carried out on the optimal planning of distributed generation (DG) systems under different aspects. There are number of important issues to be considered while carrying out studies related to the planning and operational aspects of DG. The planning of the electric system with the presence of DG requires the definition of several factors, such as: the best technology to be used, the number and the capacity of the units, the best location, the type of network connection, etc. The impact of DG in system operating characteristics, such as electric losses, voltage profile, stability and reliability needs to be appropriately evaluated. For that reason, the use of an optimization method capable of indicating the best solution for a given distribution network can be very useful for the system planning engineer, when dealing with the increase of DG penetration that is happening nowadays. The selection of the best places for installation and the preferable size of the DG units in large distribution systems is a complex combinatorial optimization problem. This paper aims at providing a review of the relevant aspects related to DG and its impact that DG might have on the operation of distributed networks. This paper covers the review of basics of DG, DG definition, current status of DG technologies, potential advantages and disadvantages, review for optimal placement of DG systems, optimizations techniques/methodologies used in optimal planning of DG in distribution systems. An attempt has been made to judge that which methodologies/techniques are suitable for optimal placement of DG systems based on the available literature and detail comparison(s) of each one. © 2012 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: DG; Distribution system; Optimal planning
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DOCUMENT TYPE: Review
SOURCE: Scopus
Pagano, C.a , Granger, E.a , Sabourin, R.a , Gorodnichy, D.O.b
Detector ensembles for face recognition in video surveillance
(2012) Proceedings of the International Joint Conference on Neural Networks, art. no. 6252659, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84865101783&partnerID=40&md5=668a3d51309fda3980354063982e6dfe
AFFILIATIONS: Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle, École de Technologie Supérieure, Université du Québec, 1100 Notre-Dame Ouest, Montreal, QC H3C 1K3, Canada;
Video Surveillance and Biometrics Section, Science and Engineering Directorate, Canada Border Services Agency, 14 Colonnade Dr., Ottawa, ON K2E 6T7, Canada
ABSTRACT: Biometric systems for recognizing faces in video streams have become relevant in a growing number of private and public sector applications, among them screening for individuals of interest in dense and moving crowds. In practice, the performance of these systems typically declines because they encounter a variety of uncontrolled conditions that change during operations, and they are designed a priori using limited data and knowledge of underlying data distributions. This paper presents multi-classifier system that can achieve a high level of performance in real-world video surveillance applications. This system assigns an ensemble of detectors (2-class classifiers) per individual, where base detectors are co-jointly trained using population-based evolutionary optimization. During enrolment of an individual, an aggregative Dynamic Niching Particle Swarm Optimization (DNPSO)-based training strategy generates a diversified homogenous pool of ARTMAP neural network classifiers using reference data samples. Classifiers associated with local optima of the aggregative DNPSO are directly selected and efficiently combined in the Receiver Operating Characteristic (ROC) space. Performance is assessed in terms of both accuracy and resource requirements on facial regions extracted from video streams of the Face in Action database. A comparison between a standard global and modular classification architectures is provided in this paper. Simulation results indicate that recognizing an individual using the aforementioned ensemble of detectors provides a scalable architecture that maintains a significantly higher level of accuracy and robustness as the number of individuals grows. © 2012 IEEE.
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Asadzadeh, M., Tolson, B.
Hybrid Pareto archived dynamically dimensioned search for multi-objective combinatorial optimization: Application to water distribution network design
(2012) Journal of Hydroinformatics, 14 (1), pp. 192-205.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84865045057&partnerID=40&md5=ca8bae929e040259c183852b36d15dfd
AFFILIATIONS: Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
ABSTRACT: Pareto archived dynamically dimensioned search (PA-DDS) has been modified to solve combinatorial multi-objective optimization problems. This new PA-DDS algorithm uses discrete-DDS as a search engine and archives all non-dominated solutions during the search. PA-DDS is also hybridized by a general discrete local search strategy to improve its performance near the end of the search. PA-DDS inherits the simplicity and parsimonious characteristics of DDS, so it has only one algorithm parameter and adjusts the search strategy to the user-defined computational budget. Hybrid PA-DDS was applied to five benchmark water distribution network design problems and its performance was assessed in comparison with NSGAII and SPEA2. This comparison was based on a revised hypervolume metric introduced in this study. The revised metric measures the algorithm performance relative to the observed performance variation across all algorithms in the comparison. The revised metric is improved in terms of detecting clear differences between approximations of the Pareto optimal front. Despite its simplicity, Hybrid PA-DDS shows high potential for approximating the Pareto optimal front, especially with limited computational budget. Independent of the PA-DDS results, the new local search strategy is also shown to substantially improve the final NSGAII and SPEA2 Pareto fronts with minimal additional computational expense. © IWA Publishing 2012.
AUTHOR KEYWORDS: Combinatorial problems; Heuristic optimization; Hypervolume performance metric; Local search; Multi-objective optimization; Water distribution network design
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DOCUMENT TYPE: Article
SOURCE: Scopus
Zhang, L.a , Xu, Y.b , Liu, Y.c
An elite decision making harmony search algorithm for optimization problem
(2012) Journal of Applied Mathematics, 2012, art. no. 860681, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864940812&partnerID=40&md5=b7d8adc3936bf584086460b83bc37738
AFFILIATIONS: Department of Mathematics, Zhejiang A and F University, Zhejiang 311300, China;
Department of Mathematics, Zhejiang Sci.-Tech. University, Zhejiang 310018, China;
State Key Laboratory of Software Engineering, Wuhan University, Hubei 430072, China
ABSTRACT: This paper describes a new variant of harmony search algorithm which is inspired by a well-known item "elite decision making". In the new algorithm, the good information captured in the current global best and the second best solutions can be well utilized to generate new solutions, following some probability rule. The generated new solution vector replaces the worst solution in the solution set, only if its fitness is better than that of the worst solution. The generating and updating steps and repeated until the near-optimal solution vector is obtained. Extensive computational comparisons are carried out by employing various standard benchmark optimization problems, including continuous design variables and integer variables minimization problems from the literature. The computational results show that the proposed new algorithm is competitive in finding solutions with the state-of-the-art harmony search variants. Copyright © 2012 Lipu Zhang et al.
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wang, H.a b c , Moon, I.b , Yang, S.c d , Wang, D.a c
A memetic particle swarm optimization algorithm for multimodal optimization problems
(2012) Information Sciences, 197, pp. 38-52.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862815257&partnerID=40&md5=dd4a696b5912a15c76d32da8f6f60000
AFFILIATIONS: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
Department of Industrial Engineering, Pusan National University, Pusan, South Korea;
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom
ABSTRACT: Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal solutions. In this paper, a memetic algorithm that hybridizes particle swarm optimization (PSO) with a local search (LS) technique, called memetic PSO (MPSO), is proposed for locating multiple global and local optimal solutions in the fitness landscape of MMOPs. Within the framework of the proposed MPSO algorithm, a local PSO model, where the particles adaptively form different species based on their indices in the population to search for different sub-regions in the fitness landscape in parallel, is used for globally rough exploration, and an adaptive LS method, which employs two different LS operators in a cooperative way, is proposed for locally refining exploitation. In addition, a triggered re-initialization scheme, where a species is re-initialized once converged, is introduced into the MPSO algorithm in order to enhance its performance of solving MMOPs. Based on a set of benchmark functions, experiments are carried out to investigate the performance of the MPSO algorithm in comparison with some EAs taken from the literature. The experimental results show the efficiency of the MPSO algorithm for solving MMOPs. © 2012 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Local search; Memetic algorithm; Multimodal optimization problem; Particle swarm optimization; Species
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DOCUMENT TYPE: Article
SOURCE: Scopus
Sayed, O.M.a , Soliman, O.S.b , Gendy, T.S.c , Mohamed, S.M.c
Memetic particle swarm optimization algorithm for multi-objective optimization problems
(2012) 2012 8th International Conference on Informatics and Systems, INFOS 2012, art. no. 6236596, pp. MM111-MM118.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864843209&partnerID=40&md5=9671e827383705d9cb09518d1f4e644a
AFFILIATIONS: Department of Operations Research, El-Asher University, Egypt;
Faculty of Computers and Information, Cairo University, Egypt;
Egyptian Petroleum Research Institute (EPRI), Cairo, Egypt
ABSTRACT: In this paper, we propose a new Memetic Particle Swarm Optimization scheme that Incorporates random walk for local search techniques In the non-dominated sorting Particle Swarm Optimization algorithm In addition to the mechanism of crowding distance computation, resulting in an efficient and effective optimization method. The proposed algorithm has been applied to different unconstrained and constrained programming problems and the obtained results are compared to that of the published ones. The results showed that the proposed approach generates a precise well distributed set of non-dominated solutions Justifying the superiority of the random walk method Memetic approach. © 2012 Cairo University.
AUTHOR KEYWORDS: Crowding Distance; Local search; Memetic algorithm; Metric space; Multi-objective Optimization; Non-dominated sorting algorithm; Particle Swarm Optimization; Random Walk method
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Chen, Y.a , Mazlack, L.b , Lu, L.c
Learning fuzzy cognitive maps from data by ant colony optimization
(2012) GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, pp. 9-16.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864714121&partnerID=40&md5=db4f8c13d7505954bf15f91e95c6be6c
AFFILIATIONS: School of Electronic and Computing Systems, University of Cincinnati, 2600 Clifton Ave., Cincinnati, OH 45221, United States;
Applied Computational Intelligence Laboratory, University of Cincinnati, 2600 Clifton Ave., Cincinnati, OH 45221, United States;
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, United States
ABSTRACT: Fuzzy Cognitive Maps (FCMs) are a flexible modeling technique with the goal of modeling causal relationships. Traditionally FCMs are developed by experts. We need to learn FCMs directly from data when expert knowledge is not available. The FCM learning problem can be described as the minimization of the difference between the desired response of the system and the estimated response of the learned FCM model. Learning FCMs from data can be a difficult task because of the large number of candidate FCMs. A FCM learning algorithm based on Ant Colony Optimization (ACO) is presented in order to learn FCM models from multiple observed response sequences. Experiments on simulated data suggest that the proposed ACO based FCM learning algorithm is capable of learning FCM with at least 40 nodes. The performance of the algorithm was tested on both single response sequence and multiple response sequences. The test results are compared to several algorithms, such as genetic algorithms and nonlinear Hebbian learning rule based algorithms. The performance of the ACO algorithm is better than these algorithms in several different experiment scenarios in terms of model errors, sensitivities and specificities. The effect of number of response sequences and number of nodes is discussed. © 2012 ACM.
AUTHOR KEYWORDS: ant colony optimization; data-driven learning algorithm; fuzzy cognitive maps; numerical optimization
REFERENCES: Kosko, B., Fuzzy cognitive maps (1986) International Journal of Man-Machine Studies, 24 (1), pp. 65-75;
Stach, W., Kurgan, L., Pedrycz, W., Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps Proceedings of the 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008, (Hong Kong, China, June 1 - June 6, 2008), pp. 1975-1981;
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M., Genetic learning of fuzzy cognitive maps (2005) Fuzzy Sets and Systems, 153 (3), pp. 371-401;
Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.S., Groumpos, P.P., Vrahatis, M.N., Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization (2005) Journal of Intelligent Information Systems, 25, pp. 95-121;
Ghazanfari, M., Alizadeh, S., Fathian, M., Koulouriotis, D.E., Comparing simulated annealing and genetic algorithm in learning FCM (2007) Applied Mathematics and Computation, 192 (1), pp. 56-68;
Stach, W., Kurgan, L., Pedrycz, W., A divide and conquer method for learning large fuzzy cognitive maps (2010) Fuzzy Sets and Systems, 161 (19), pp. 2515-2532;
Stach, W., (2010) Learning and Aggregation of Fuzzy Cognitive Maps - An Evolutionary Approach, , PhD Dissertation, University of Alberta;
Chandra Mohan, B., Baskaran, R., A survey: Ant Colony Optimization based recent research and implementation on several engineering domain (2012) Expert Systems with Applications, 39 (4), pp. 4618-4627;
Dorigo, M., Maniezzo, V., Colorni, A., Ant system: Optimization by a colony of cooperating agents (1996) Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26 (1), pp. 29-41;
Liao, T., Oca, M.A.M.D., Aydin, D., Stützle, T., Dorigo, M., An incremental ant colony algorithm with local search for continuous optimization Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (Dublin, Ireland, 2011), pp. 125-132;
Xiao-Min, H., Jun, Z., Chung, H.S.H., Yun, L., Ou, L., SamACO: Variable Sampling Ant Colony Optimization Algorithm for Continuous Optimization (2010) Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 40 (6), pp. 1555-1566;
Ding, Z., Li, D., Jia, J., First Study of Fuzzy Cognitive Map Learning Using Ants Colony Optimization (2011) Journal of Computational Information Systems, 7 (13), pp. 4756-4763;
Chen, N., Zhang, J., Index-based genetic algorithm for continuous optimization problems Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (Dublin, Ireland, 2011), pp. 1029-1036;
Tsadiras, A.K., Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps (2008) Information Sciences, 178 (20), pp. 3880-3894;
Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P., Active Hebbian learning algorithm to train fuzzy cognitive maps (2004) International Journal of Approximate Reasoning, 37 (3), pp. 219-249;
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M., Learning fuzzy cognitive maps using evolution strategies: A novel schema for modeling and simulating high-level behavior Proceedings of the Congress on Evolutionary Computation (Soul, Korea, Republic Of, 2001), pp. 364-371;
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M., Evolutionary development of fuzzy cognitive maps Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2005 (Reno, NV, United States, May 22 - May 25, 2005), pp. 619-624;
Parsopoulos, K.E., Papageorgiou, E.I., Groumpos, P.P., Vrahatis, M.N., A first study of fuzzy cognitive maps learning using particle swarm optimization Proceedings of the the 2003 Congress on Evolutionary Computation, 2003. CEC '03 (2003), pp. 1440-1447;
Alizadeh, S., Ghazanfari, M., Jafari, M., Hooshmand, S., Learning FCM by Tabu Search (2007) International Journal of Computer Science, 2 (2), pp. 142-149;
Alizadeh, S., Ghazanfari, M., Learning FCM by chaotic simulated annealing (2009) Chaos, Solitons & Fractals, 41 (3), pp. 1182-1190;
Christian, B., Ant colony optimization: Introduction and recent trends (2005) Physics of Life Reviews, 2 (4), pp. 353-373;
Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P., Fuzzy cognitive map learning based on nonlinear Hebbian rule Proceedings of the Australian Conference on Artificial Intelligence (2003), pp. 256-268
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Horng, S.-C.a , Yang, F.-Y.b , Lin, S.-S.c
Applying PSO and OCBA to minimize the overkills and re-probes in wafer probe testing
(2012) IEEE Transactions on Semiconductor Manufacturing, 25 (3), art. no. 6203424, pp. 531-540.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864711142&partnerID=40&md5=cd75cd4984af8a71a0f100d78d65dbab
AFFILIATIONS: Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan;
Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 112, Taiwan;
Department of Electrical Engineering, St. John's University, Taipei 25135, Taiwan
ABSTRACT: In this paper, the problem of minimizing overkills and re-probes in wafer probe testing is formulated as a multiobjective optimization problem. Overkill is a measure of good dies that were considered bad and re-probe is an additional manual probe testing to save overkills. The goal is to provide an optimal setting of threshold values for engineers to decide whether to carry out a re-probe after the two times of automatic probe testing. A two-stage algorithm is proposed to take advantage of particle swarm optimization (PSO) and optimal computing budget allocation (OCBA) for solving a good enough setting that minimizes overkills and re-probes within a reasonable computational time. A crude model based on a shorter stochastic simulation with a small number of test wafers is used as a fitness evaluation in a PSO algorithm to select N good enough settings. Then, we proceed with the refined OCBA to search for a good enough setting. The two-stage algorithm is applied to a real semiconductor product, and the threshold values obtained by the proposed algorithm are promising in the aspects of solution quality and computational efficiency. We have also demonstrated the computational efficiency of our algorithm by comparing with the genetic algorithm and evolution strategy. © 1988-2012 IEEE.
AUTHOR KEYWORDS: Multiobjective optimization; optimal computing budget allocation (OCBA); overkill; particle swarm optimization (PSO); re-probe; wafer probe testing
REFERENCES: Liu, Y., Luk, T., Irving, S., Parameter modeling for wafer probe test (2009) IEEE Trans. Electron. Packag. Manuf., 32 (2), pp. 81-88., Apr;
Wang, F., Cheng, R., Li, X.X., MEMS vertical probe cards with ultradensely arrayed metal probes for wafer-level IC testing (2009) J. Microelectromech. Syst., 18 (4), pp. 933-941., Aug;
Skinner, K.R., Montgomery, D.C., Runger, G.C., Fowler, J.W., McCarville, D.R., Rhoads, T.R., Stanley, J.D., Multivariate statistical methods for modeling and analysis of wafer probe test data (2002) IEEE Trans. Semicond. Manuf., 15 (4), pp. 523-530., Nov;
Hsieh, Y.L., Tzeng, G.H., Lin, T.R., Yu, H.C., Wafer sort bitmap data analysis using the PCA-based approach for yield analysis and optimization (2010) IEEE Trans. Semicond. Manuf., 23 (4), pp. 493-502., Nov;
Wang, C.H., Separation of composite defect patterns on wafer bin map using support vector clustering (2009) Expert Syst. Appl., 36 (2), pp. 2554-2561., Mar;
Lee, J.H., Ha, S.H., Recognizing yield patterns through hybrid applications of machine learning techniques (2009) Inform. Sci., 179 (6), pp. 844-850., Mar;
Hsu, S.C., Chien, C.F., Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing (2007) Int. J. Prod. Econ., 107 (1), pp. 88-103., May;
Cakir, B., Altiparmak, F., Dengiz, B., Multiobjective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm (2011) Comput. Ind. Eng., 60 (3), pp. 376-384., Apr;
Duarte, A., Laguna, M., Marti, R., Tabu search for the linear ordering problem with cumulative costs (2011) Comput. Optim. Appl., 48 (3), pp. 697-715., Apr;
Horng, S.C., Yang, F.Y., Lin, S.S., Embedding evolutionary strategy in ordinal optimization for hard optimization problems (2012) Appl. Math. Model., 36 (8), pp. 3753-3763., Aug;
Goldberg, D.E., Sastry, K., (2010) Genetic Algorithms: The Design of Innovation, , 2nd ed. New York: Springer-Verlag, Apr;
Parsopoulos, K., Vrahatis, M., Particle swarm optimization method in multiobjective problems (2002) Proc. 16th ACM Symp. Appl. Comput., pp. 603-607., Mar;
Chen, C.C., Two-layer particle swarm optimization for unconstrained optimization problems (2011) Appl. Soft. Comput., 11 (1), pp. 295-304., Jan;
Chen, S.H., Jakeman, A.J., Norton, J.P., Artificial intelligence techniques: An introduction to their use for modeling environmental systems (2008) Math. Comput. Simul., 78 (2-3), pp. 379-400., Jul;
Lin, S.Y., Horng, S.C., Application of an ordinal optimization algorithm to the wafer testing process (2006) IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, 36 (6), pp. 1229-1234., Nov;
Ho, Y.C., Zhao, Q.C., Jia, Q.S., (2007) Ordinal Optimization: Soft Optimization for Hard Problems, , New York: Springer-Verlag, Sep;
Chen, C.H., Lee, L.H., (2010) Stochastic Simulation Optimization: An Optimal Computing Budget Allocation, , Hackensack, NJ: World Scientific, Aug;
He, D.H., Lee, L.H., Chen, C.H., Fu, M., Wasserkrug, S., Simulation optimization using the cross-entropy method with optimal computing budget allocation (2010) ACM Trans. Model. Comput. Simul., 20 (1), p. 4., Jan;
Chen, C.H., Yuecesan, E., Dai, L.Y., Chen, H.C., Optimal budget allocation for discrete-event simulation experiments (2010) IIE Trans., 42 (1), pp. 60-70., Jan;
Lee, L.H., Chew, E.P., Teng, S.Y., Computing budget allocation rules for multiobjective simulation models based on different measures of selection quality (2011) Automatica, 46 (12), pp. 1935-1950., Dec;
Teng, S.Y., Lee, L.H., Chew, E.P., Integration of indifference-zone with multiobjective computing budget allocation (2010) Eur. J. Oper. Res., 203 (2), pp. 419-429., Jun;
Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G., Particle swarm optimization: Basic concepts, variants and applications in power systems (2008) IEEE Trans. Evol. Comput., 12 (2), pp. 171-195., Apr;
Lin, S.Y., Ho, Y.C., Universal alignment probability revisited (2002) J. Optim. Theory Appl., 113 (2), pp. 399-407., May;
Chen, C.H., Wu, S.D., Dai, L., Ordinal comparison of heuristic algorithms using stochastic optimization (1999) IEEE Trans. Robot. Autom., 15 (1), pp. 44-56., Feb;
Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization for integer programming (2002) Proc. IEEE Cong. Evol. Comput., 2, pp. 1582-1587., May;
Chen, D.B., Zhao, C.X., Particle swarm optimization with adaptive population size and its application (2009) Appl. Soft. Comput., 9 (1), pp. 39-48., Jan;
Chow, S.C., Shao, J., Wan, H.S., (2007) Sample Size Calculations in Clinical Research, , 2nd ed. Boca Raton, FL Chapman and Hall/CRC, Aug;
Montgomery, D.C., (2008) Introduction to Statistical Quality Control, 6th ed, , New York: Wiley, May
DOCUMENT TYPE: Article
SOURCE: Scopus
Lien, L.-C.a , Cheng, M.-Y.b
A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization
(2012) Expert Systems with Applications, 39 (10), pp. 9642-9650.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859214537&partnerID=40&md5=453d535bd56aa4126bbaf868fe1c4e33
AFFILIATIONS: National Taiwan University of Science and Technology, #43, Keelung Rd., Taipei 106, Taiwan;
Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Keelung Rd., Taipei 106, Taiwan
ABSTRACT: The construction site layout (CSL) design presents a particularly interesting area of study because of its relatively high level of attention to usability qualities, in addition to common engineering objectives such as cost and performance. However, it is difficult combinatorial optimization problem for engineers. Swarm intelligence (SI) was very popular and widely used in many complex optimization problems which was collective behavior of social systems such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study proposed an optimization hybrid swarm algorithm namely particle-bee algorithm (PBA) based on a particular intelligent behavior of honey bee and bird swarms by integrates theirs advantages. This study compares the performance of PBA with that of BA and PSO for hypothetical construction engineering of CSL problems. The results show that the performance of PBA is comparable to those of the mentioned algorithms and can be efficiently employed to solve those hypothetical CSL problems with high dimensionality. © 2012 Published by Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Bee algorithm; Construction site layout; Particle swarm optimization; Particle-bee algorithm; Swarm intelligence
REFERENCES: Abdinnour-Helm, S., Hadley, S.W., Tabu search based heuristics for multi-floor construction site layout (2000) International Journal of Production Research, 38 (2), pp. 365-383;
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Cheng, M.Y., (1992) Automated Site Layout of Temporary Construction Facilities Using Geographic Information Systems (GIS), , PhD thesis, University of Texas at Austin, Tex;
Cheng, M.Y., Lien, L.C., A hybrid AI approach particle bee algorithm (PBA) for complex optimization problems Applied Mathematics and Computation (SCI/EI), , (in preparation);
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Li, X.L., (2003) A New Intelligent Optimization - Artificial Fish Swarm Algorithm, , PhD thesis, Zhejiang University of Zhejiang, China;
Li, H., Love, P.E.D., Genetic search for solving construction site-level unequal-area construction site layout problems (2000) Automation in Construction, 9 (2), pp. 217-226;
Michalek, J.J., Choudhary, R., Papalambros, P.Y., Architectural layout design optimization (2002) Engineering Optimization, 34 (5), pp. 461-484;
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Pham, D.T., Koc, E., Ghanbarzadeh, A., Otri, S., Rahim, S., Zaidi, M., The bees algorithm - A novel tool for complex optimization problems (2006) Proceedings of the Second International Virtual Conference on Intelligent Production Machines and Systems, pp. 454-461;
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Yeh, I.-C., Architectural layout optimization using annealed neural network (2006) Automation in Construction, 15 (4), pp. 531-539
DOCUMENT TYPE: Article
SOURCE: Scopus
Kitayama, S., Yamazaki, K.
Compromise point incorporating trade-off ratio in multi-objective optimization
(2012) Applied Soft Computing Journal, 12 (8), pp. 1959-1964.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861898933&partnerID=40&md5=82a308f7abac4e744f4d31bb3d7bd603
AFFILIATIONS: Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
ABSTRACT: The aims of multi-objective optimization are (1) to find pareto-optimal solutions and (2) to analyze the trade-off between conflicting objectives. This paper proposes an interactive method for solving multi-objective optimization problems using the satisficing trade-off method (STOM). In particular, we introduce a trade-off matrix to quantitatively analyze the trade-off between conflicting objectives. Each element of the trade-off matrix consists of a projection matrix of active constraints and gradients of objective functions. In addition, the compromise point and the compromise solution incorporating the trade-off ratio that the decision-maker desires are defined in this paper. The technique to obtain the compromise point is proposed in this paper. Through numerical examples, the validity proposed method is examined. © 2012 Elsevier B.V.
AUTHOR KEYWORDS: Compromise point; Multi-objective optimization; Trade-off ratio
REFERENCES: Miettinen, K.M., (1998) Nonlinear Multiobjective Optimization, , Kluwer Academic Publishers;
Deb, K., (2001) Multi-Objective Optimization Using Evolutionary Algorithms, , Wiley;
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Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method in multiobjective problems (2002) Proceedings of the ACM Symposium on Applied Computing, pp. 603-607;
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Yang, J.-B., Chen, C., Zhang, Z.-J., Interactive step trade-off method (ISTM) for multiobjective optimization (1990) IEEE Transactions on Systems, Man and Cybernetics, 20 (3), pp. 688-695., DOI 10.1109/21.57283;
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Branke, J., Deb, K., Miettinen, K., Slowinski, R., (2008) Multiobjective Optimization: Interactive and Evolutionary Approaches, Lecture Nodes in Computer Science, , Springer-Verlag;
Nakayama, H., Sawaragi, Y., (1984) Satisficing Trade-Off Method for Multiobjective Programming, Lecture Notes in Economics and Mathematical Systems 229, , Springer-Verlag pp. 113-122;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Du, J.-J.a b , Yuan, P.a , Ma, L.-Z.a , Wu, W.a , Yang, X.-L.a b
A novel design methodology for active shim coil
(2012) Measurement Science and Technology, 23 (8), art. no. 085502, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863906255&partnerID=40&md5=44a012771cb57165d2c281cff13c2738
AFFILIATIONS: Institute of Modern Physics of Chinese Academy of Sciences (IMPCAS), Lanzhou, China;
Graduate University of Chinese Academy of Sciences (GUCAS), Beijing, China
ABSTRACT: A novel design approach for active shimming coils for superconducting magnets is proposed to compensate for the previous ten components of the field deviation. The analytic method is first used to obtain the topologies of coils of various order fields and establish a coil model. Then the particle swarm optimization method is adopted to optimize parameters, and the deviation of the magnetic field is taken as the fitness function for minimization of the bias of a magnetic field. The results from the analytic method are taken as a reference to set the initial value ranges of parameters. The results have shown that, compared with the traditional analytic method, the coils with this method can generate a field of better quality. Also the method involves less internal memory and CPU usage than the pure numerical algorithm. In addition, it has fast searching ability and demonstrates high efficiency; and the global solution can be effectively found, which facilitates directly winding. © 2012 IOP Publishing Ltd.
AUTHOR KEYWORDS: Fourier series; ion trap; MRI; shim coil; target field
REFERENCES: Wu, W., He, Y., Ma, L.Z., Huang, W.X., Yao, Q.G., Wu, X., Guo, B.L., Xia, J.W., Design of a 7 T superconducting magnet for Lanzhou Penning trap (2010) IEEE Trans. Appl. Supercond., 20, pp. 989-992., 10.1109/TASC.2010.2040957 1051-8223;
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Zhang, Y.L., Xie, D.X., Xia, P.C., Passive shimming and shape optimization of an unconventional permanent magnet for MRI (2003) 6th Int. Conf. Electrical Machines and Systems, ICEMS 2003, pp. 899-902;
Liu, F., Zhu, J.F., Xia, L., A hybrid field-harmonics approach for passive shimming design in MRI (2011) IEEE Trans. Appl. Supercond., 21, pp. 60-67., 10.1109/TASC.2011.2112358 1051-8223;
Juchem, C., Muller-Bierl, B., Schick, F., Logothetis, N.K., Pfeuffer, J., Combined passive and active shimming for in vivo MR spectroscopy at high magnetic fields (2006) Journal of Magnetic Resonance, 183 (2), pp. 278-289., DOI 10.1016/j.jmr.2006.09.002, PII S1090780706002746;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Baghaee, H.R.a , Mirsalim, M.a b , Gharehpetian, G.B.a , Kaviani, A.K.c
Security/cost-based optimal allocation of multi-type FACTS devices using multi-objective particle swarm optimization
(2012) Simulation, 88 (8), pp. 999-1010.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864248730&partnerID=40&md5=eb9a3244e6b6926b4cd37d2c4aa97b85
AFFILIATIONS: Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran;
Engineering Department, Saint Mary's University, TX, United States;
ECE Department, College of Engineering and Computing, Florida International University, Miami, FL, United States
ABSTRACT: Flexible alternating current transmission system (FACTS) devices can regulate the active and reactive power as well as voltage-magnitude. Placement of these devices in suitable locations can lead to the control of line power flow, bus voltages and short circuit currents at desired levels and, as a result, improvement of power system security margins. This paper presents an optimal allocation algorithm for FACTS devices based on a novel m-objective particle swarm optimization method considering both power system costs and security. The proposed algorithm has successfully been applied to an IEEE 30-bus power system and the results are presented and discussed. © 2012 The Society for Modeling and Simulation International.
AUTHOR KEYWORDS: FACTS devices; multi-objective particle swarm optimization; power system security; transmission systems
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DOCUMENT TYPE: Article
SOURCE: Scopus
Fernández Martínez, J.L.a b c , García Gonzalo, E.a , Fernández Muñiz, Z.a , Mukerji, T.b
How to design a powerful family of particle swarm optimizers for inverse modelling
(2012) Transactions of the Institute of Measurement and Control, 34 (6), pp. 705-719. Cited 1 time.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857468548&partnerID=40&md5=04e8b4a7af9215ed43b8292668df92da
AFFILIATIONS: Department of Mathematics, University of Oviedo, Oviedo, Spain;
Energy Resources Engineering Department, Stanford University, Palo Alto, CA, United States;
Department of Civil and Environmental Engineering, University of California Berkeley, Berkeley, United States
ABSTRACT: In this paper, we show how to design a powerful set of particle swarm optimizers to be applied in inverse modelling. The design is based on the interpretation of the swarm dynamics as a stochastic damped mass-spring system, the so-called particle swarm optimization (PSO) continuous model. Based on this idea we derived a family of PSO optimizers (GPSO, CC-PSO and CP-PSO) having different exploitation and exploration capabilities. Their convergence is related to the stability of their first (mean trajectories)- and second-order moments (variance and temporal covariance). Good parameter sets are located inside their first stability regions close to the upper border of their respective second stability regions where the attraction from the particles oscillation centre is very weak. In this region of weak attraction, both convergence to the global minimum and exploration of the search space are possible. Based on this idea, we have designed a particle-cloud algorithm where each particle in the swarm has different inertia (damping) and acceleration (rigidity) constants. We explored the performance of these algorithms for different PSO members using different benchmark functions, showing that the cloud algorithms have a very good balance between exploration and exploitation. Also, the cloud design helps to avoid two main drawbacks of the PSO algorithm: the tuning of the PSO parameters and the clamping of the particles velocities. We also present the lime and sand algorithm that changes the time step with iterations. This feature helps to avoid entrapment in local minima when the time step is increased, and enables exploration around the global best when the time step is decreased. All these designs are based on the theoretical analysis of the PSO dynamics. We explain how to use this knowledge to the solution and appraisal of inverse problems. Finally, we briefly introduce the combined use of PSO and model reduction techniques to allow posterior sampling in high dimensional spaces. © The Author(s) 2011.
AUTHOR KEYWORDS: Cloud particle swarm; exploitation; global optimization; sampling
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Eberhart, R., Shi, Y., Comparing inertia weights and constriction factors in particle swarm optimization Proceedings of the Congress on Evolutionary Computation;
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Shi, Y., Eberhart, R., A modified particle swarm optimizer Proceedings of the 1998 IEEE World Congress on Computational Intelligence, Conference on Evolutionary Computation;
Shi, Y., Eberhart, R.C., Parameter Selection in Particle Swarm Optimization (1998) Lecture Notes In Computer Science, (1447), pp. 591-600., Evolutionary Programming VII;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Sedki, A., Ouazar, D.
Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems
(2012) Advanced Engineering Informatics, 26 (3), pp. 582-591.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863778537&partnerID=40&md5=92518b8be376913ec0a3ef8ea6f58649
AFFILIATIONS: Department of Civil Engineering, Mohammadia School of Engineering, University Mohammed V-Agdal, 765, Agdal, Rabat, Morocco
ABSTRACT: Water distribution system design belongs to a class of large combinatorial non-linear optimization problems, involving complex implicit constraints, such as conservation of mass and energy equations, which are commonly satisfied through the use of hydraulic simulation solvers. Recently, many researchers have shifted the focus from traditional optimization methods to the use of meta-heuristic approaches for handling this complexity. This paper proposes a hybrid particle swarm optimization (PSO) and differential evolution (DE) method, linked to the hydraulic simulator, EPANET, for minimizing the cost design of water distribution systems. The performance of the proposed PSO-DE algorithm is demonstrated using three well-known benchmark water distribution system problems, the two-loop network, the Hanoi network and the New York Tunnels network. The results are compared to that of standard PSO and previously applied optimization methods. It is found that PSO-DE is a promising method for solving water distribution system design problems as it outperforms standard PSO and other algorithms previously presented in the literature for the three case studies considered. © 2012 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Differential evolution; Particle swarm optimization; Water distribution systems
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DOCUMENT TYPE: Article
SOURCE: Scopus
Ali, M.a , Pant, M.a , Abraham, A.b
A simplex differential evolution algorithm: Development and applications
(2012) Transactions of the Institute of Measurement and Control, 34 (6), pp. 691-704.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864257610&partnerID=40&md5=91091625537b5e9934ce46188155132e
AFFILIATIONS: Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Saharanpur - 247001, India;
Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, United States
ABSTRACT: Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The conventional method for generating the initial population is the use of computer-generated pseudo-random numbers, which may not be very effective. In the present study, we have investigated the potential of generating the initial population by integrating the non-linear simplex method of Nelder and Mead with pseudo-random numbers in a DE algorithm. The resulting algorithm named the non-linear simplex DE is tested on a set of 20 benchmark problems with box constraints and two real life problems. Numerical results show that the proposed scheme for generating the random numbers significantly improves the performance of DE in terms of fitness function value, convergence rate and average CPU time. © The Author(s) 2011.
AUTHOR KEYWORDS: Crossover; differential evolution; initial population; random numbers; stochastic optimization
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Pant, M., Ali, M., Singh, V.P., Parent centric differential evolution algorithm for global optimization (2009) Opsearch, 46 (2), pp. 153-168;
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Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A., Opposition based-differential evolution (2008) IEEE Transactions on Evolutionary Computation, 12 (1), pp. 64-79;
Rogalsky, T., Derksen, R.W., Kocabiyik, S., Differential evolution in aerodynamic optimization Proceedings of the 46th Annual Conference of the Canadian Aeronautics and Space Institute;
Storn, R., Price, K., DE-A simple and efficient adaptive scheme for global optimization over continuous space (1995) Technical Report TR-95-012, , ftp.icsi.berkeley.edu/pub/techreports/1995/tr-95-012.ps.Z, ICSI, March 1995;
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Wang, C.-X., Cui, D.-W., Wan, D.-S., Wang, L., A novel genetic algorithm based on gene therapy theory (2006) Transactions of the Institute of Measurement and Control, 28 (3), pp. 253-262., DOI 10.1191/0142331206tim172oa;
Zhu, Y., He, X., Hu, K., Niu, B., Information entropy based interaction model and optimization method for swarm intelligence (2009) Transactions of the Institute of Measurement and Control, 31 (6), pp. 461-474
DOCUMENT TYPE: Article
SOURCE: Scopus
Liu, Y.a b , Wang, H.a , Ji, Y.c , Liu, Z.a , Zhao, X.a
Land use zoning at the county level based on a multi-objective particle swarm optimization algorithm: A case study from Yicheng, China
(2012) International Journal of Environmental Research and Public Health, 9 (8), pp. 2801-2826.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84865088137&partnerID=40&md5=fea4005024bed1be8c85413c81ba7c52
AFFILIATIONS: School of Resource and Environmental Science, Wuhan University, Luoyu Road 129, Wuhan 430079, China;
Key Laboratory of Geographical Information System, Ministry of Education, Wuhan University, Luoyu Road 129, Wuhan 430079, China;
Department of Computer Science, Central China Normal University, Luoyu Road 152, Wuhan 430079, China
ABSTRACT: Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can generate a corresponding optimal solution, with a different quantity structure and spatial pattern to satisfy the preference of the different decision makers; (7) the model proposed in this paper is capable of handling the land-use zoning problem, and the crossover and mutation operator can improve the performance of the model, but, nevertheless, leads to increased time consumption. © 2012 by the authors; licensee MDPI, Basel, Switzerland.
AUTHOR KEYWORDS: Crossover and mutation; Land-use zoning; Multi-objective optimization; Particle swarm optimization; Yicheng
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DOCUMENT TYPE: Article
SOURCE: Scopus
Shao, H.a , Wang, Z.b , Xu, W.c
Obtain semi-definite matrix eigenvalue based on LANCZOS algorithm
(2012) Advances in Intelligent and Soft Computing, 148 AISC (VOL. 1), pp. 253-259.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864321954&partnerID=40&md5=c12c1c592264dcd009e9983b5ab40e32
AFFILIATIONS: Jianghan University School of Physics and Information, Wuhan, China;
Hubei Water Resources Research Institute, Wuhan, China;
Yangtze River Scientific Research Institute, Wuhan, China
ABSTRACT: Orthogonal projection lanczos algorithm is the effective way to solve the complex structural vibration, vibration frequency and vibration mode. Its idea is to translate high-level vibration problem into low-level to solve the vibration problem without losing eigenvalue. In this paper, a simple and convenient method of computing is presented using Orthogonal Projection lanczos algorithm to solve semi-definite matrix generalized eigenvalue problems and re-solve the eigenvalue. © 2012 Springer-Verlag GmbH.
AUTHOR KEYWORDS: eigenvalue; finite element method; orthogonalization; structure
REFERENCES: Zhang, H.X., (1999) Automobile Design, , China Machine Press, Beijing;
Qin, X., Jiang, H., A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters (2005) Journal of Parallel and Distributed Computing, 65 (8), pp. 885-900;
Huang, K., (1999) Advanced Engineering Mathematics, , People Railway Publishing House, Beijing;
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Reyes-Sierra, M., Coello, C.A.C., Fitness Inheritance in Multi-objective
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Abedinia, O.a , Amjady, N.a , Naderi, M.S.b
Optimal congestion management in an electricity market using Modified Invasive Weed Optimization
(2012) 2012 11th International Conference on Environment and Electrical Engineering, EEEIC 2012 - Conference Proceedings, art. no. 6221423, pp. 467-472.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864228676&partnerID=40&md5=9d38dbe0beda79e175216a17a2c733db
AFFILIATIONS: Electrical Engineering Department, Semnan University, Semnan, Iran;
School of Electrical Engineering and Telecommunication, UNSW, Sydney, Australia
ABSTRACT: This paper presents optimal congestion management in an electricity market using Modified Invasive Weed Optimization (MIWO). The IWO is a bio-inspired numerical technique which is inspired from weed colonization and motivated by a common phenomenon in agriculture that is colonization of invasive weeds. Transmission pricing and congestion management are the key elements of a competitive electricity market based on direct access. They also focus of much of the debate concerning alternative approaches to the market design and the implementation of a common carrier electricity system. This paper focuses on the tradeoffs between simplicity and economic efficiency in meeting the objectives of a transmission pricing and congestion management scheme. The effectiveness of the proposed technique is applied on 30 and 118 bus IEEE standard power system in comparison with CPSO, PSO-TVAC and PSO-TVIW. The numerical results demonstrate that the proposed technique is better and superior than other compared methods. © 2012 IEEE.
AUTHOR KEYWORDS: MIWO; Operating limits; Optimal congestion management
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Kumar, A., Srivastava, S.C., Singh, S.N., Congestion management in competitive power market: A bibliographical survey (2005) Electric Power Systems Research, 76, pp. 153-164;
Capitanescu, F., Cutsem, T.V., A unified management of congestions due to voltage instability and thermal overloads (2007) Electric Power Systems Research, 77, pp. 1274-1283;
Meena, T., Selvi, K., Cluster based congestion management in deregulated electricity market using PSO (2005) Proceeding of IEEE Indicon Conference, December, 11 (13), pp. 627-630;
Fu, J., Lamont, J.W., A combined framework for service identification and congestion management (2001) IEEE Transactions on Power Systems, 16, pp. 56-61;
Yesuratnam, G., Thukaram, D., Congestion management in open access based on relative electrical distances using voltage stability criteria (2007) Electric Power Systems Research, 77, pp. 1608-1618;
Kumar, A., Srivastava, S.C., Singh, S.N., A zonal congestion management approach using ac transmission congestion distribution factors (2004) IEEE Transactions on Power Systems, 72, pp. 85-93;
Yu, C.N., Ilic, M., Congestion clusters-based markets for transmission management (1999) Proceeding of IEEE PES Winter Meeting, 2, pp. 821-832;
Mehrabian, A.R., Lucas, C., A novel numerical optimization algorithm inspired from invasive weed colonization (2006) Ecological Informatics, 1, pp. 355-366;
Sepehri-Rad, H., Lucas, C., A recommender system based on invasive weed optimization algorithm (2007) Proc. IEEE Congress on Evolutionary Computation, pp. 4297-4304;
Mehrabian, A.R., Yousefi-Koma, A., Optimal positioning of piezoelectric actuators of smart fin using bio-inspired algorithms (2007) Aerospace Science and Technology, 11, pp. 174-182;
Panida, B., Chanwit, B., Weerakorn, O., Optimal congestion management in an electricity market using particle swarm optimization with time-varying acceleration coefficients (2010) Computers and Mathematics with Applications, 3, pp. 1-10;
Pavlidis, N.G., Parsopoulos, K.E., Vrahatis, M.N., Computing Nash equilibria through computational intelligence methods (2005) J. of Computational and Applied Mathematics, 175 (1), pp. 113-136;
Dadalipour, B., Mallahzadeh, A.R., Davoodi-Rad, Z., Application of the invasive weed optimization technique for antenna configurations (2008) Proc. Loughborough Antennas and Propagation Conf., Loughborough, pp. 425-428., Mar;
Zhang, X., Wang, Y., Cui, G., Niu, Y., Xu, J., Application of a novel IWO to the design of encoding sequences for DNA computing (2008) Computers and Mathematics with Applications, , doi:10.1016/j.camwa.2008.10.038;
Panida, B., Chanwit, B., Weerakorn, O., Optimal congestion management in an electricity market using particle swarm optimization with timevarying acceleration coefficients (2010) Computers and Mathematics with Applications, 3, pp. 1-10
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Jiang, X., Ling, H.
A new multiobjective particle swarm optimizer with fuzzy learning sub-swarms and self-adaptive parameters
(2012) Advanced Science Letters, 7, pp. 696-699.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864195764&partnerID=40&md5=0757099bc8f5b9bbd1fa00001298ea96
AFFILIATIONS: Department of Mechanical Engineering, PLA University of Science and Technology, Nanjing 210007, China
ABSTRACT: Multiobjective optimization (MOO) problem are usually very computationally expensive since there are usually exponentially large Pareto-optimal solutions. The paper presents a new multiobjective particle swarm optimization (MOPSO) algorithm which uses fuzzy learning sub-swarms and self-adaptive parameters to improve the overall search ability. During the search process, each particle in the swarm can have a sub-swarm of p particles which are sequentially generated based on fuzzy-controlled parameters, and a fuzzy satisfying solution is chosen to replace the particle in the next generation of the swarm. Numerical experiments and case studies demonstrate that our approach can achieve good solution qualities with low computational costs. © 2012 American Scientific Publishers. All Rights Reserved.
AUTHOR KEYWORDS: Fuzzy learning; Multiobjective optimization (MOO); Particle swarm optimization (PSO); Sub-swarm
REFERENCES: Pardalos, P.M., Migdalas, A., Pitsoulis, L., (2008) Pareto Optimality, , (eds.), Game Theory and Equilibria, Springer, New York;
Chinchuluun, A., Pardalos, P.M., (2007) Ann. Oper. Res., 154, p. 29;
Kennedy, J., Eberhart, R., (1995) Proceeding IEEE International Conference Neural Networks, p. 1942., Perth WA, Australia;
Parsopoulos, K.E., Vrahatis, M.N., (2002) Proc. ACM Symp. Applied Computing, p. 603;
Li, X., (2003) Lect. Note. Comp. Sci., 2723, p. 37;
Liu, D., Tan, K.C., Goh, C.K., Ho, W.K., (2007) IEEE Tran. Sys. Man. Cyber., 37, p. 42;
Tripathi, P.K., Bandyopadhyay, S., Pal, S.K., (2007) Information Sci., 177, p. 5033;
Goh, C.K., Tan, K.C., Liu, D.S., Chiam, S.C., (2010) Euro. J. Oper. Res., 202, p. 42;
Cooren, Y., Clerc, M., Siarry, P., (2011) Comput. Optim. App., 49, p. 379;
Wei, J.X., (2009) Evolutionary algorithms for solving single-objective and multiobjective optimization problems, , Ph. D Thesis, Xi'an Dianzi University;
Zhan, Z.H., Zhang, J.J., Li, Y., Chung, H.S.H., (2009) IEEE Tran. Sys. Man. Cyber., 39, p. 1362;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Silva, A.a , Neves, A.a , Gonçalves, T.b
An heterogeneous particle swarm optimizer with predator and scout particles
(2012) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7326 LNAI, pp. 200-208.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864128991&partnerID=40&md5=76051b321d7626090c626c2b1469d804
AFFILIATIONS: Escola Superior de Tecnologia, Instituto Politécnico de Castelo Branco, Portugal;
Universidade de Évora, Portugal
ABSTRACT: We present a new heterogeneous particle swarm optimization algorithm, called scouting predator-prey optimizer. This algorithm uses the swarm's interactions with a predator particle to control the balance between exploration and exploitation. Scout particles are proposed as a straightforward way of introducing new exploratory behaviors into the swarm. These can range from new heuristics that globally improve the algorithm to modifications based on problem specific knowledge. The scouting predator-prey optimizer is compared with several variations of both particle swarm and differential evolution algorithms on a large set of benchmark functions, selected to present the algorithms with different difficulties. The experimental results suggest the new optimizer can outperform the other approaches over most of the benchmark problems. © 2012 Springer-Verlag.
AUTHOR KEYWORDS: heterogeneous particle swarms; particle swarm optimization; swarm intelligence
REFERENCES: Angeline, P.J., Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences (1998) LNCS, 1447, pp. 601-610., Porto, V.W., Waagen, D. (eds.) EP 1998. Springer, Heidelberg;
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Engelbrecht, A., Heterogeneous Particle Swarm Optimization (2010) LNCS, 6234, pp. 191-202., Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. Springer, Heidelberg;
Gao, H., Xu, W., A new particle swarm algorithm and its globally convergent modifications (2011) IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 41 (5), pp. 1334-1351;
Gao, H., Xu, W., Particle swarm algorithm with hybrid mutation strategy (2011) Applied Soft Computing, 11 (8), pp. 5129-5142;
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De Montes Oca, M., Pena, J., Stutzle, T., Pinciroli, C., Dorigo, M., Heterogeneous particle swarm optimizers (2009) IEEE Congress on Evolutionary Computation, CEC 2009, pp. 698-705., May;
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Wang, H., Li, H., Liu, Y., Li, C., Zeng, S., Opposition-based particle swarm algorithm with cauchy mutation (2007) IEEE Congress on Evolutionary Computation, CEC 2007, pp. 4750-4756., September
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Jananisri, D.a , Kalyanasundaram, M.b , Gopinath, B.b
Damping of power system oscillations using unified power flow controller
(2012) IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012, art. no. 6216169, pp. 528-533.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863979883&partnerID=40&md5=b0c2b69091c8432b8a1cbef9bce0e71d
AFFILIATIONS: Power Systems Engineering, Vivekanandha College of Engineering for Women, Namakkal, Tamilnadu, India;
Department of EEE, Vivekanandha College of Engineering for Women, Namakkal, Tamilnadu, India
ABSTRACT: This paper presents an approach for the determination of the optimal parameters and placement of UPFC as the major concern is to ensure the full potential of utilization in the transmission network. Voltage source model is utilized to study the behaviour of the UPFC in regulating the active, reactive power and voltage profile. This model is incorporated in Newton Raphson algorithm for load flow studies. This paper proposes particle swarm optimization for the exact real power loss minimization including UPFC. Installing UPFC with such optimal parameters will eliminate or minimize the overloaded lines and the bus voltage violations under critical contingencies. The implementation of loss minimization for the optimal location of UPFC was tested with IEEE-14 bus system. © 2012 Pillay Engineering College.
AUTHOR KEYWORDS: Flexible ac transmission systems (FACTS); Loss minimization; Particle Swarm Optimization (PSO); Unified Power Flow Controller (UPFC)
REFERENCES: Kannan, A.S., Kayalvizhi, R., Utility of PSO for loss minimization and Enhancement of voltage profile using UPFC (2011) International Journal of Scientific & Engineering Research, 2 (2)., February;
Rajabi-Ghahanavieh, A., Fotuhi-Firuzabad, M., Shahidehpour, M., Feuillet, R., UPFC for enhancing power system reliability (2010) IEEE Transactions on Power Delivery, 25 (4)., October;
Kowsalya, M., Ray, K.K., Kothari, D.P., Loss optimization for voltage stability enhancement incorporating UPFC using particle swarm optimization (2009) Journal of Electrical Engineering & Technology, 4 (4), pp. 492-498;
Tara Kalyani, S., Tulasiram Das, G., Simulation of real and reactive power flow control with UPFC connected to atransmission line Journal of Theoretical and Applied Infonnation Technology © 2008;
Parasopoulos, K.E., Vrahatis, M.N., On the computation of all global minimizers through particle swarm optimization (2004) IEEE Trans. on Evolutionary Computation, 8 (3)., June;
Fuerte-Esquivel, C.R., Acha, E., Ambriz-Perez, H., Comprehensive Newton-Raphson UPFC model for the quadratic power flow solution ofpractical power networks (2000) IEEE Trans. on Power Systems, 15 (1), pp. 102-109., Feb;
Liu, J.-Y., Song, Y.-H., Mehta, P.A., Strategies for handling UPFC constraints in steady-state power flow and voltage control (2000) IEEE Transactions on Power Systems, 15 (2)., May;
Kawata, K., Fukuyama, Y., (1999) A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability, , Japan;
Hingorani, N.G., Gyugyi, L., (1999) Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems, , Wiley-IEEE Press;
Fuerte-Esquivel, C.R., Acha, E., Unified power flow controller: A critical comparison of Newton-Raphson UPFC algorithms in power flow studies (1997) LEE Proc.-Gener. Transm. Distrib., 144 (5)., September;
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Kennedy, Eberhart, R., Particle swarm optimization (1995) Proc. of IEEE International Conference on Neural Networks, 4, pp. 1942-1948., Perth, Australia;
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Mubeen, S.E., Nema, R.K., Agnihotri, G., Power flow control with UPFC in power transmission system (2008) World Academy of Science, Engineering and Technology, 47
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Ashrafi, K.a , Shafiepour, M.a , Ghasemi, L.a , Najar Araabi, B.b
Prediction of climate change induced temperature rise in regional scale using neural network
(2012) International Journal of Environmental Research, 6 (3), pp. 677-688.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863968304&partnerID=40&md5=17590f5bf1d9c5cb98e2105bf933e872
AFFILIATIONS: Graduate Faculty of Environment, University of Tehran, P.O.BOX 14155-6135, Tehran, Iran;
Faculty of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
ABSTRACT: The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict temperature rise due to climate change in regional scale. In the present work data recorded over years 1985-2008 have been used at training and testing steps for ANN model. The multilayer perceptron (MLP) network architecture is used for this purpose. Three applied optimization methods are backpropagation (BP) (in both input selection and weight optimization), genetic algorithm (GA) (in both input selection and weight optimization) and combined GA-particle swarm optimization (PSO) (input selection by GA and weight optimization by PSO). In this framework, natural and anthropogenic parameters which affect the incoming solar radiation are considered in order to predict the climate change induced temperature rise in regional scale. Inputs of ANN model are mean temperature, dew point temperature, relative humidity, wind speed, solar radiation, cloudiness, rainfall, station-level pressure (QFE) and greenhouse gases. For predicting monthly mean temperature, input data include one month, six months, 12 months and 24 months before recorded data. In this work, nine stations namely Tehran, Mashhad, Ramsar, Orumiyeh, Sanandaj, Yazd, Ahwaz, Bandar Abbas and Chabahar in nine different climatic region of Iran are chosen to determine the temperature rise over Iran. Results show that the averaged minimum square errors (MSE) are 0.0196, 0.0224 and 0.0228 for ANN-BP, ANN-GA and ANN-GA-PSO methods, respectively. The ANN model associated with BP optimization method predict annual mean temperature rise as 0.44, 0.49, 0.20, 0.12, 0.17, 0.46, 0.41, 0.06 and 0.01 °C after 10 years for mentioned stations, respectively. These values show the average temperature rise of 0.26 °C after 10 years (the base year is 2008) for Iran.
AUTHOR KEYWORDS: Back propagation; Climate change; Genetic algorithm; Neural network; Particle swarm optimization; Temperature rise
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DOCUMENT TYPE: Article
SOURCE: Scopus
Rocha, A.M.A.C.a d , Costa, M.F.P.b c , Fernandes, E.M.G.P.d
An artificial fish swarm filter-based method for constrained global optimization
(2012) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7335 LNCS (PART 3), pp. 57-71.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863885277&partnerID=40&md5=c00937d207c3969d740416c1519d0621
AFFILIATIONS: Department of Production and Systems, School of Engineering, University of Minho, Braga 4710-057, Portugal;
Department of Mathematics and Applications, School of Sciences, University of Minho, Braga 4710-057, Portugal;
Mathematics R and D Centre, University of Minho, Braga 4710-057, Portugal;
Algoritmi R and D Centre, University of Minho, Braga 4710-057, Portugal
ABSTRACT: An artificial fish swarm algorithm based on a filter methodology for trial solutions acceptance is analyzed for general constrained global optimization problems. The new method uses the filter set concept to accept, at each iteration, a population of trial solutions whenever they improve constraint violation or objective function, relative to the current solutions. The preliminary numerical experiments with a well-known benchmark set of engineering design problems show the effectiveness of the proposed method. © 2012 Springer-Verlag.
AUTHOR KEYWORDS: Artificial Fish Swarm; Filter Method; Global optimization; Swarm intelligence
REFERENCES: Aguirre, A.H., Rionda, S.B., Coello Coello, C.A., Lizárraga, G.L., Montes, E.M., Handling constraints using multiobjective optimization concepts (2004) International Journal for Numerical Methods in Engineering, 59, pp. 1989-2017;
Akhtar, S., Tai, K., Tay, T., A socio-behavioural simulation model for engineering design optimization (2002) Engineering Optimization, 34, pp. 341-354;
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Audet, C., Dennis Jr., J.E., A pattern search filter method for nonlinear programming without derivatives (2004) SIAM Journal on Optimization, 14 (4), pp. 980-1010;
Azad, M.A.K., Fernandes, E.M.G.P., Rocha, A.M.A.C., Nonlinear continuous global optimization by modified differential evolution (2010) International Conference of Numerical Analysis and Applied Mathematics 2010, 1281, pp. 955-958., Simos, T.E., et al. (eds.);
Azad, M.A.K., Fernandes, E.M.G.P., Modified Differential Evolution Based on Global Competitive Ranking for Engineering Design Optimization Problems (2011) LNCS, 6784, pp. 245-260., Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part III. Springer, Heidelberg;
Barbosa, H.J.C., Lemonge, A.C.C., An adaptive penalty method for genetic algorithms in constrained optimization problems (2008) Frontiers in Evolutionary Robotics, pp. 9-34., Iba, H. (ed.) I-Tech Education Publ., Austria;
Birgin, E.G., Floudas, C.A., Martinez, J.M., Global minimization using an augmented Lagrangian method with variable lower-level constraints (2010) Mathematical Programming, 125, pp. 139-162;
Chootinan, P., Chen, A., Constrained handling in genetic algorithms using a gradient-based repair method (2006) Computers and Operations Research, 33, pp. 2263-2281;
Coello C.A, Use of a self-adaptive penalty approach for engineering optimization problems (2000) Computers in Industry, 41, pp. 113-127;
Costa, M.F.P., Fernandes, E.M.G.P., Assessing the potential of interior point barrier filter line search methods: Nonmonotone versus monotone approach (2011) Optimization, 60 (10-11), pp. 1251-1268;
Costa, M.F.P., Fernandes, E.M.G.P., On Minimizing Objective and KKT Error in a Filter Line Search Strategy for an Interior Point Method (2011) LNCS, 6784, pp. 231-244., Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part III. Springer, Heidelberg;
Deb, K., An efficient constraint handling method for genetic algorithms (2000) Computer Methods in Applied Mechanics and Engineering, 186, pp. 311-338;
Fernandes, E.M.G.P., Martins, T.F.M.C., Rocha, A.M.A.C., Fish swarm intelligent algorithm for bound constrained global optimization (2009) CMMSE 2009, pp. 461-472., Aguiar, J.V. (ed.);
Fletcher, R., Leyffer, S., Nonlinear programming without a penalty function (2002) Mathematical Programming, 91, pp. 239-269;
Hedar, A.-R., Fukushima, M., Heuristic pattern search and its hybridization with simulated annealing for nonlinear global optimization (2004) Optimization Methods and Software, 19, pp. 291-308;
Hedar, A.-R., Fukushima, M., Derivative-free filter simulated annealing method for constrained continuous global optimization (2006) Journal of Global Optimization, 35, pp. 521-549;
Gao, X.Z., Wu, Y., Zenger, K., Huang, X., A knowledge-based artificial fish-swarm algorithm (2010) 13th IEEE International Conference on Computational Science and Engineering, pp. 327-332;
Jiang, M., Mastorakis, N., Yuan, D., Lagunas, M.A., Image segmentation with improved artificial fish swarm algorithm (2009) LNEE, 28, pp. 133-138., Mastorakis, N., et al. (eds.) ECC 2008;
Jiang, M., Wang, Y., Pfletschinger, S., Lagunas, M.A., Yuan, D., Optimal Multiuser Detection with Artificial Fish Swarm Algorithm (2007) CCIS, 2, pp. 1084-1093., Huang, D.-S., et al. (eds.) ICIC 2007. Springer, Heidelberg;
Kaelo, P., Ali, M.M., A numerical study of some modified differencial evolution algorithms (2006) European Journal of Operational Research, 169, pp. 1176-1184;
Karaboga, D., Basturk, B., Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems (2007) LNCS (LNAI), 4529, pp. 789-798., Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. Springer, Heidelberg;
Karimi, A., Nobahari, H., Siarry, P., Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions (2010) Computational Optimization and Applications, 45, pp. 639-661;
Liu, J.-L., Lin, J.-H., Evolutionary computation of unconstrained and constrained problems using a novel momentum-type particle swarm optimization (2007) Engineering Optimization, 39, pp. 287-305;
Mahdavi, M., Fesanghary, M., Damangir, E., An improved harmony search algorithm for solving optimization problems (2007) Applied Mathematics and Computation, 188, pp. 1567-1579;
Mallipeddi, R., Suganthan, P.N., Ensemble of constraint handling techniques (2010) IEEE Transactions on Evolutionary Computation, 14, pp. 561-579;
Petalas, Y.G., Parsopoulos, K.E., Vrahatis, M.N., Memetic particle swarm optimization (2007) Annals of Operations Research, 156, pp. 99-127;
Pereira, A.I., Costa, M.F.P., Fernandes, E.M.G.P., Interior point filter method for semi-infinite programming problems (2011) Optimization, 60 (10-11), pp. 1309-1338;
Rocha, A.M.A.C., Fernandes, E.M.G.P., Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems (2009) International Journal of Computer Mathematics, 86, pp. 1932-1946;
Rocha, A.M.A.C., Fernandes, E.M.G.P., Martins, T.F.M.C., Novel Fish Swarm Heuristics for Bound Constrained Global Optimization Problems (2011) LNCS, 6784, pp. 185-199., Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part III. Springer, Heidelberg;
Rocha, A.M.A.C., Fernandes, E.M.G.P., Numerical study of augmented Lagrangian algorithms for constrained global optimization (2011) Optimization, 60 (10-11), pp. 1359-1378;
Rocha, A.M.A.C., Martins, T.F.M.C., Fernandes, E.M.G.P., An augmented Lagrangian fish swarm based method for global optimization (2011) Journal of Computational and Applied Mathematics, 235 (16), pp. 4611-4620;
Runarsson, T.P., Yao, X., Stochastic ranking for constrained evolutionary optimization (2000) IEEE Transaction on Evolutionary Computation, 4, pp. 284-294;
Silva, E.K., Barbosa, H.J.C., Lemonge, A.C.C., An adaptive constraint handling technique for differential evolution with dynamic use of variants in engineering optimization (2011) Optimization and Engineering, 12, pp. 31-54;
Socha, K., Dorigo, M., Ant colony optimization for continuous domains (2008) European Journal of Operational Research, 185, pp. 1155-1173;
Stanoyevitch, A., Homogeneous genetic algorithms (2010) International Journal of Computer Mathematics, 87, pp. 476-490;
Ulbrich, M., Ulbrich, S., Vicente, L.N., A globally convergent primal-dual interiorpoint filter method for nonlinear programming (2004) Mathematical Programming, 100, pp. 379-410;
Wächter, A., Biegler, L.T., On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming (2006) Mathematical Programming, 106, pp. 25-57;
Wang, C.-R., Zhou, C.-L., Ma, J.-W., An improved artificial fish-swarm algorithm and its application in feed-forward neural networks (2005) Proceedings of the 4th ICMLC, pp. 2890-2894;
Wang, Y., Cai, Z., Zhou, Y., Fan, Z., Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique (2009) Structural and Multidisciplinary Optimization, 37 (4), pp. 395-413;
Wang, X., Gao, N., Cai, S., Huang, M., An Artificial Fish Swarm Algorithm Based and ABC Supported QoS Unicast Routing Scheme in NGI (2006) LNCS, 4331, pp. 205-214., Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds.) ISPAWorkshops 2006. Springer, Heidelberg;
Zahara, E., Hu, C.-H., Solving constrained optimization problems with hybrid particle swarm optimization (2008) Engineering Optimization, 40 (11), pp. 1031-1049
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Sjarif, N.N.A., Shamsuddin, S.M., Hashim, S.Z.M.
A framework of multi-objective particle swarm optimization in motion segmentation problem
(2012) 2012 2nd International Conference on Digital Information and Communication Technology and its Applications, DICTAP 2012, art. no. 6215337, pp. 93-98.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863688465&partnerID=40&md5=0a0f9710df46e6394620786867d696c0
AFFILIATIONS: Soft Computing Reseach Group, Universiti Teknologi Malaysia, Skudai, Malaysia
ABSTRACT: Research in motion segmentation and robust tracking have been getting more attention recently. In video sequence, motion segmentation is considered as multi-objective problem. Better representation and processing of the standard image in video sequence, with efficient segmentation algorithm is required. Thus, multi-objective optimization approach is an appropriate method to solve the optimization problem in motion segmentation. In this paper, we present new framework of the video surveillance for optimization of motion segmentation using Multi-objective particle swarm (MOPSO) algorithm. Experiment based on benchmarked test functions of MOPSO and PSO is evaluated to show the result with respect to the coverage metric of the best point of optimization value. The result indicates that MOPSO is highly good in converging towards the Pareto Front and has generated a well-distributed set of non-dominated solution. Hence, is a promising solution in multi-objective motion segmentation problem of video surveillance application. © 2012 IEEE.
AUTHOR KEYWORDS: Motion segmentation; Multiobjective optimization; Multiobjective Particle Swarm Optimzation (MOPSO)
REFERENCES: Spagnolo, P., Orazio, T.D., Leo, M., Distante, A., Moving object segmentation by background subtraction and temporal analysis (2006) Image and Vision Computing, 24 (5), pp. 411-423., DOI 10.1016/j.imavis.2006.01.001, PII S026288560600014X;
Maddalena, L., Petrosino, A., A Self-Organizing Approach to Background Subtraction for Visual Surveillance Application (2008) Image Processing, IEEE Transactions, 17 (7), pp. 1168-1177;
SanMiguel, J.C., On the Evaluation of Background Subtraction Algorithms without Ground-Truth (2010) Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 180-187;
Tao, Z., Nevatia, R., Bo, W., Segmentation and Tracking of Multiple Humans in Crowded Environments (2008) IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (7), pp. 1198-1211;
Allili, M.S., Image and Video Segmentation by Combining Unsupervised Generalized Gaussian Mixture Modeling and Feature Selection (2010) IEEE Transactions on Circuits and Systems for Video Technology, 20 (10), pp. 1373-1377;
Hernandez-Lopez, F.J., Rivera, M., Binary Segmentation of Video Sequences in Real Time Ninth Mexican International Conference in Artificial Intelligence (MICAI), 2010;
Bugeau, A.L., Perez, P., Detection and segmentation of moving objects in complex scenes (2009) Computer Vision and Image Understanding, 113 (4), pp. 459-476;
Zhang, W., Wu, Q.M.J., Yin, H.B., Moving vehicles detection based on adaptive motion histogram (2010) Digital Signal Processing, 20 (3), pp. 793-805;
McHugh, J.M., Foreground-Adaptive Background Subtraction (2009) Signal Processing Letters, IEEE, 16 (5), pp. 390-393;
Bugeau, A., Perez, P., Detection and segmentation of moving objects in highly dynamic scenes IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2007;
Hongxing, G., A robust foreground segmentation method by temporal averaging multiple video frames International Conference in Audio, Language and Image Processing (ICALIP), 2008;
Lee Yee, S., Motion detection using Lucas Kanade algorithm and application enhancement International Conference in Electrical Engineering and Informatics (ICEEI), 2009;
Bong, C.-W., Rajeswari, M., Multiobjective Optimization Approaches in Image Segmentation - The Direction and Challenges (2010) International Journal of Advances in Soft Computing and Its Applications, 2 (1), pp. 40-65;
Dassatti, A., Masera, G., Piccinini, G., Low Complexity Motion Detection with Background Modeling 3rd International Conference in Information and Communication Technologies: From Theory to Applications (ICTTA), 2008;
Ming-Yu, S., Motion-based Background Modeling for Moving Object Detection on Moving Platforms (2007) Proceedings of 16th International Conference in Computer Communications and Networks (ICCCN), pp. 1178-1182;
Lim, T., Han, B., Han, J.H., Modeling and segmentation of floating foreground and background in videos (2011) Pattern Recognition;
Viet-Quoc, P., Takahashi, K., Naemura, T., Foreground-background segmentation using iterated distribution matching (2011) IEEE Conference in Computer Vision and Pattern Recognition (CVPR), pp. 2113-2120;
Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Neural Networks Proceedings., IEEE International Conference, pp. 1942-1948;
Zitzler, E., Deb, K., Thiele, L., Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (2000) Evolutionary Computation, 8, pp. 173-195;
Reyes-Sierra, M., Coello, C.A.C., Multi-Objective Particle Swarm Optimizers:A Survey of the State-of-the-Art (2006) International Journal of Computational Intelligence Research, 2 (3), pp. 287-308;
Coello, C.A.C., Lechunga, M.S., MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization (2002) IEEE Proceedings Congess on Evolutionary Computation, pp. 1051-1056;
Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method in multiobjective problems (2002) Proceedings of the Symposium on Applied Computing (ACM), Madrid, Spain, pp. 603-607;
Fieldsend, J., (2004) Multi-Objective Particle Swarm Optimization Methods, , Technical Report No. 419, Department of Computer Science, University of Exeter
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Brinson, T.E.a , Ordonez, J.C.b , Luongo, C.A.b
Optimization of an integrated SOFC-fuel processing system for aircraft Propulsion
(2012) Journal of Fuel Cell Science and Technology, 9 (4), art. no. 41006, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863449314&partnerID=40&md5=995eab57e7bf3ea98ad2b1f6af6f9b49
AFFILIATIONS: Center for Advanced Power Systems, Florida Agricultural and Mechanical University, Tallahassee, FL 32310, United States;
Department of Mechanical Engineering, FSU Center for Advanced Power Systems, Florida State University, Tallahassee, FL 32310, United States
ABSTRACT: As fuel cells continue to improve in performance and power densities levels rise, potential applications ensue. System-level performance modeling tools are needed to further the investigation of future applications. One such application is small-scale aircraft propulsion. Both piloted and unmanned fuel cell aircrafts have been successfully demonstrated suggesting the near-term viability of revolutionizing small-scale aviation. Nearly all of the flight demonstrations and modeling efforts are conducted with low temperature fuel cells; however, the solid oxide fuel cell (SOFC) should not be overlooked. Attributing to their durability and popularity in stationary applications, which require continuous operation, SOFCs are attractive options for long endurance flights. This study presents the optimization of an integrated solid oxide fuel cell-fuel processing system model for performance evaluation in aircraft propulsion. System parameters corresponding to maximum steady state thermal efficiencies for various flight phase power levels were obtained through implementation of the particle swarm optimization (PSO) algorithm. Optimal values for fuel utilization, air stoichiometric ratio, air bypass ratio, and burner ratio, a four-dimensional optimization problem, were obtained while constraining the SOFC operating temperature to 650-1000°C. The PSO swarm size was set to 35 particles, and the number of iterations performed for each case flight power level was set at 40. Results indicate the maximum thermal efficiency of the integrated fuel cell-fuel processing system remains in the range of 44-46% throughout descend, loitering, and cruise conditions. This paper discusses a system-level model of an integrated fuel cell-fuel processing system, and presents a methodology for system optimization through the particle swarm algorithm. Copyright © 2012 by American Society of Mechanical Engineers.
REFERENCES: Poshusta, J., Li, Z., Mahoney, J., Kodzwa, P., Fuel processing for portable sofc systems (2008) Fuel Cell Seminar and Exposition, 2008 (1852)., Phoenix, AZ, FCSE Paper;
Brett, D.J.L., Aguiar, P., Brandon, N.P., System modelling and integration of an intermediate temperature solid oxide fuel cell and ZEBRA battery for automotive applications (2006) Journal of Power Sources, 163 (1 SPEC. ISS.), pp. 514-522., DOI 10.1016/j.jpowsour.2006.08.036, PII S0378775306017551;
Larminie, J., Dicks, A., (2003) Fuel Cell Systems Explained, , 2nd ed., John Wiley and Sons, West Sussex;
Pukrushpan, J.T., (2003) Modeling and Control of Fuel Cell Systems and Fuel Processors, , Ph.D. thesis, University of Michigan Ann Arbor, MI;
Tsourapas, V., (2007) Control Analysis of Integrated Fuel Cell Systems With Energy Recuperation Devices, , Ph.D. thesis, University of Michigan Ann Arbor, MI;
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Holtappels, P., Mehling, H., Roehlich, S., Liebermann, S.S., Stimming, U., SOFC system operating strategies for mobile applications (2005) Fuel Cells, 5 (4), pp. 499-508., DOI 10.1002/fuce.200400088;
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Eberhart, R.C., Kennedy, J., A new optimizer using particle swarm theory (1995) Proceedings of the 6th International Symposium on Micro Machine and Human Science IEEE Service Center, pp. 39-43., Piscataway, NJ;
Reynolds, C.W., Flocks, herds, and schools: A distributed behavioral model (1987) ACM Comput. Graph., 21 (4), pp. 25-34;
Shi, Y., Particle swarm optimization (2004) IEEE Connect, Newsletter IEEE Neural Networks Soc., 2 (1), pp. 8-13;
Zamfirescu, C., Dincer, I., Thermodynamic performance analysis and optimization of a sofc-h+ system (2009) Thermochim. Acta, 486, pp. 32-40;
Vargas, J.V.C., Ordonez, J.C., Bejan, A., Constructal flow structure for a PEM fuel cell (2004) International Journal of Heat and Mass Transfer, 47 (19-20), pp. 4177-4193., DOI 10.1016/j.ijheatmasstransfer.2004.05.004, PII S0017931004001838;
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Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method in multiobjective problems (2002) Proceedings of the ACM Symposium on Applied Computing, pp. 603-607
DOCUMENT TYPE: Article
SOURCE: Scopus
Danziger, M., Amaral Henriques, M.A.
Computational intelligence applied on cryptology: A brief review
(2012) IEEE Latin America Transactions, 10 (3), art. no. 6222587, pp. 1798-1810.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862995014&partnerID=40&md5=8b3b47d3ebcc302eba555878877a53f3
AFFILIATIONS: Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
ABSTRACT: Many cryptographic techniques have been developed and several were broken. Recently, new models have arisen with different and more complex approaches to cryptography and cryptanalysis, like those based on the Computational Intelligence (CI). Different bio-inspired techniques can be found in the literature showing their effectiveness in handling hard problems in the area of cryptology. However, some authors recognize that the advances have been slow and that more efforts are needed to take full advantage of CI techniques. In this work, we present a brief review of some of the relevant works in this area. The main objective is to better understand the advantages of applying CI on cryptology in the search for new ways of improving computer security © 2012 IEEE.
AUTHOR KEYWORDS: Artificial Immune Systems; Artificial Neural Network; Cellular Automata; Computational Intelligence; Cryptography; Cryptology; DNA Computing; Evolutionary Computation
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DOCUMENT TYPE: Review
SOURCE: Scopus
Amaya, I., Cruz, J., Correa, R.
Solution of the mathematical model of a nonlinear direct current circuit using particle swarm optimization [Solución del modelo matemático de un circuito electrónico no lineal en corriente directa mediante optimización por enjambre de partículas]
(2012) DYNA (Colombia), 79 (172), pp. 77-84.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862842012&partnerID=40&md5=d97bc88406be30f03517ff3b0338e659
AFFILIATIONS: Universidad Industrial de Santander, Colombia
ABSTRACT: This article describes a numeric strategy focused on the solution of nonlinear systems of equations, frequently found in the analysis of electronic circuits. This strategy is based on the use of the particle swarm optimization (PSO) algorithm, as an alternative to the traditional Newton-Raphson. First, and as a demonstrative example, a circuit composed of two resistors and a diode were considered. Afterwards, a more complex one comprising one current source, four resistors, and two diodes was implemented. Based on the results, it was observed that the solution alternative is very attractive for solving these kinds of circuits, regardless of their size and complexity.
AUTHOR KEYWORDS: Direct current; Mathematical model; Non-linear electronic circuit; Particle swarm optimization
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DOCUMENT TYPE: Article
SOURCE: Scopus
Zhai, J.a , Wang, K.b
Particle swarm optimization with Baldwin effect for different dimension multimodal optimization problems
(2012) Journal of Convergence Information Technology, 7 (12), pp. 186-194.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864112005&partnerID=40&md5=59207f70372cdbeea7606f31722f45f8
AFFILIATIONS: School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China;
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China
ABSTRACT: Particle Swarm Optimization (PSO) is an effective optimizing technique simulating the collective intelligence of the particle swarm. However, it often suffers from being trapped into local optima when solving complex multimodal optimizing problems. The paper proposed a novel particle swarm optimization with Baldwin effect (PSO-BE) for solving different dimension multimodal optimization problems. Search behavior analysis indicates that PSO-BE has a larger potential search space than that of the simple PSO. Experimental simulation have done on many variants of PSO, the widely investigations demonstrated that PSO-BE outperforms other algorithms on most complex multi-modal problems with different dimensions.
AUTHOR KEYWORDS: Computational intelligence; Multi-modal optimization; Particle swarm optimization
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Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Proceeding(s) of IEEE International Conference On Neural Networks, pp. 1942-1948;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wilken, D., Rabbel, W.
On the application of Particle Swarm Optimization strategies on Scholte-wave inversion
(2012) Geophysical Journal International, 190 (1), pp. 580-594.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862209271&partnerID=40&md5=2cd73638995a0d9b69b71bdd4dbaf408
AFFILIATIONS: Department of Geophysics, Institute for Geosciences, Christian-Albrechts University, Kiel, Germany
ABSTRACT: We investigate different aspects concerning the application of swarm intelligence optimization to the inversion of Scholte-wave phase-slowness frequency (p-f) spectra with respect to shear wave velocity structure. Besides human influence due to the dependence on a priori information for starting models and interpretation of p-f spectra as well as noise, the model resolution of the inversion problem is strongly influenced by the multimodality of the misfit function. We thus tested the efficiency of global, stochastic optimization approaches with focus on swarm intelligence methods that can explore the multiple minima of the misfit function. A comparison among different PSO schemes by applying them to synthetic Scholte-wave spectra led to a hybrid of Particle Swarm Optimization and Downhill Simplex providing the best resolution of inverted shear wave velocity depth models. The results showed a very low spread of best fitting solutions of 7per cent in shear wave velocity and an average of 9per cent for noisy synthetic data and a very good fit to the true synthetic model used for computation of the input data. To classify this method we also compared the probability of finding a good fit in synthetic spectra inversion with that of Evolutionary Algorithm, Simulated Annealing, Neighbourhood Algorithm and Artificial Bee Colony algorithm. Again the hybrid optimization scheme showed its predominance. The usage of stochastic algorithms furthermore allowed a new way of misfit definition in terms of dispersion curve slowness residuals making the inversion scheme independent on Scholte-wave mode identification and allowing joint inversion of fundamental mode and higher mode information. Finally we used the hybrid optimization scheme and the misfit calculation for the inversion of 2-D shear wave velocity profiles from two locations in the North- and Baltic Sea. The models show acceptable resolution and a very good structural correlation to high resolution reflection seismic data. © 2012 The Authors Geophysical Journal International © 2012 RAS.
AUTHOR KEYWORDS: Europe; Inverse theory; Surface waves and free oscillations
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DOCUMENT TYPE: Article
SOURCE: Scopus
Budinská, I., Kasanický, T., Zelenka, J.
Production planning and scheduling by means of artificial immune systems and particle swarm optimisation algorithms
(2012) International Journal of Bio-Inspired Computation, 4 (4), pp. 237-248.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864255177&partnerID=40&md5=7353f08120b103506a26aaeba7999579
AFFILIATIONS: Institute of Informatics, Slovak Academy of Scientist, Dúbravská cesta 9, 845 07, Bratislava, Slovakia
ABSTRACT: The main task of the scheduling optimisation process in production systems is to minimise production cost, overall production time and to ensure optimal utilisation of the resources. Application of stochastic search techniques to find a feasible schedule that minimise cost and satisfy all constraints jointed with all products can bring a particular solution of the complexity problem. On the other hand, the cost and the time of an optimisation process have to reciprocate with the found schedule; otherwise the optimisation loses its meaning. The article presents two stochastic methods, based on biologically inspired techniques, applied on a scheduling optimisation process. The first one is based on the mechanism inspired by biological evolution and the one method applies the swarm intelligence. The application of methods is illustrated on a real world example of a production line. Copyright © 2012 Inderscience Enterprises Ltd.
AUTHOR KEYWORDS: AISs; Artificial immune systems; Bio-inspired computation; Particle swarm optimisation algorithm; Production planning and scheduling
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DOCUMENT TYPE: Article
SOURCE: Scopus
Shi, H.-Y.a b , Wang, W.-L.a , Kwok, N.-M.c , Chen, S.-Y.a
Game theory for wireless sensor networks: A survey
(2012) Sensors (Switzerland), 12 (7), pp. 9055-9097.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864350685&partnerID=40&md5=8f7c7c423ffc4ab03335196a41712874
AFFILIATIONS: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;
School of Computer Science and Technology, Shaoxing University, Shaoxing 312000, China;
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
ABSTRACT: Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field. © 2012 by the authors; licensee MDPI, Basel, Switzerland.
AUTHOR KEYWORDS: Game theory; Mechanism; Optimization; Scheduling; Wireless sensor network
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DOCUMENT TYPE: Review
SOURCE: Scopus
Matott, L.S.a , Tolson, B.A.b , Asadzadeh, M.b
A benchmarking framework for simulation-based optimization of environmental models
(2012) Environmental Modelling and Software, 35, pp. 19-30.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860151277&partnerID=40&md5=889c396787d908c811ee3a268ae361a6
AFFILIATIONS: University at Buffalo, Center for Computational Research, Buffalo, NY 14023, United States;
University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
ABSTRACT: Simulation models assist with designing and managing environmental systems. Linking such models with optimization algorithms yields an approach for identifying least-cost solutions while satisfying system constraints. However, selecting the best optimization algorithm for a given problem is non-trivial and the community would benefit from benchmark problems for comparing various alternatives. To this end, we propose a set of six guidelines for developing effective benchmark problems for simulation-based optimization. The proposed guidelines were used to investigate problems involving sorptive landfill liners for containing and treating hazardous waste. Two solution approaches were applied to these types of problems for the first time - a pre-emptive (i.e. terminating simulations early when appropriate) particle swarm optimizer (PSO), and a hybrid discrete variant of the dynamically dimensioned search algorithm (HD-DDS). Model pre-emption yielded computational savings of up to 70% relative to non-pre-emptive counterparts. Furthermore, HD-DDS often identified globally optimal designs while incurring minimal computational expense, relative to alternative algorithms. Results also highlight the usefulness of organizing decision variables in terms of cost values rather than grouping by material type. © 2012 Elsevier Ltd.
AUTHOR KEYWORDS: Benchmark problems; Dynamically dimensioned search; Model pre-emption; Particle swarm optimization; Simulation-based optimization; Sorptive barrier design
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DOCUMENT TYPE: Article
SOURCE: Scopus
Daneshyari, M.a , Yen, G.G.b
Cultural-based particle swarm for dynamic optimisation problems
(2012) International Journal of Systems Science, 43 (7), pp. 1284-1304.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861819308&partnerID=40&md5=c18e767a7bed72787d3b345279d86d66
AFFILIATIONS: Department of Technology, Elizabeth City State University, Elizabeth City, NC 27909, United States;
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, United States
ABSTRACT: Many practical optimisation problems are with the existence of uncertainties, among which a significant number belong to the dynamic optimisation problem (DOP) category in which the fitness function changes through time. In this study, we propose the cultural-based particle swarm optimisation (PSO) to solve DOP problems. A cultural framework is adopted incorporating the required information from the PSO into five sections of the belief space, namely situational, temporal, domain, normative and spatial knowledge. The stored information will be adopted to detect the changes in the environment and assists response to the change through a diversity-based repulsion among particles and migration among swarms in the population space, and also helps in selecting the leading particles in three different levels, personal, swarm and global levels. Comparison of the proposed heuristics over several difficult dynamic benchmark problems demonstrates the better or equal performance with respect to most of other selected state-of-the-art dynamic PSO heuristics. © 2012 Taylor & Francis.
AUTHOR KEYWORDS: belief space; cultural algorithm; dynamic optimisation problem; particle swarm optimisation; repulsive migration
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wang, H.a b , Yang, S.c d , Ip, W.H.e , Wang, D.a b
A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems
(2012) International Journal of Systems Science, 43 (7), pp. 1268-1283.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861805907&partnerID=40&md5=e9fa247fecce99b023292ab56c0d500e
AFFILIATIONS: College of Information Science and Engineering, Northeastern University, Shenyang 110004, China;
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, China;
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom;
College of Mathematics and Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China;
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
ABSTRACT: Many real-world optimisation problems are both dynamic and multi-modal, which require an optimisation algorithm not only to find as many optima under a specific environment as possible, but also to track their moving trajectory over dynamic environments. To address this requirement, this article investigates a memetic computing approach based on particle swarm optimisation for dynamic multi-modal optimisation problems (DMMOPs). Within the framework of the proposed algorithm, a new speciation method is employed to locate and track multiple peaks and an adaptive local search method is also hybridised to accelerate the exploitation of species generated by the speciation method. In addition, a memory-based re-initialisation scheme is introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms taken from the literature. The experimental results show the efficiency of the proposed algorithm for DMMOPs. © 2012 Taylor & Francis.
AUTHOR KEYWORDS: dynamic multi-modal optimisation problem; local search; memetic algorithm; memetic computing; particle swarm optimisation; speciation
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DOCUMENT TYPE: Article
SOURCE: Scopus
Gao, J.a b , Feng, E.a , Xiu, Z.c
Metabolic system identification and optimization in continuous culture
(2012) International Journal of Computer Mathematics, 89 (10), pp. 1426-1444.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862680672&partnerID=40&md5=9b3ec6b7dd261002fbce604ba68e68e2
AFFILIATIONS: School of Mathematical Science, Dalian University of Technology, Dalian, Liaoning, 116024, China;
School of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, 266510, China;
Department of Biotechnology, Dalian University of Technology, Dalian, Liaoning, 116012, China
ABSTRACT: To date, there still exist some uncertain factors in the continuous fermentation of glycerol to 1,3-Propanediol (1,3-PD) by Klebsiella pneumoniae because of the limitation in bio-techniques. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of the substrate and the product across the cell membrane of the biomass. On the basis of different inferences of transport mechanisms, we reconstruct various metabolic systems and develop their dynamical systems. To determine the most reasonable metabolic system from all possible ones, we give a quantitative definition of biological robustness and propose an identification model on this basis. An improved Particle Swarm Optimization algorithm is developed to solve the identification model. Numerical results show that the identified system can describe the fermentation process well. Furthermore, to maximize the concentration of 1,3-PD, an optimization model is proposed. Numerical results show that the concentration of 1,3-PD can be increased considerably by employing the obtained optimal strategy. © 2012 Copyright Taylor and Francis Group, LLC.
AUTHOR KEYWORDS: biological robustness; continuous culture; IPSO algorithm; metabolic system identification; nonlinear dynamical system
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DOCUMENT TYPE: Article
SOURCE: Scopus
Backlund, P.B., Shahan, D.W., Seepersad, C.C.
A comparative study of the scalability of alternative metamodelling techniques
(2012) Engineering Optimization, 44 (7), pp. 767-786.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862545033&partnerID=40&md5=526f54b6b5b414d5ea571c2605ff00ae
AFFILIATIONS: Department of Mechanical Engineering, University of Texas at Austin, 1 University Station C2200, Austin, TX, 78712-0292, United States
ABSTRACT: Metamodels, also known as surrogate models, can be used in place of computationally expensive simulation models to increase computational efficiency for the purposes of design optimization or design space exploration. The accuracy of these metamodels varies with the scale and complexity of the underlying model. In this article, three metamodelling methods are evaluated with respect to their capabilities for modelling high-dimensional, nonlinear, multimodal functions. Methods analyzed include kriging, radial basis functions, and support vector regression. Each metamodelling technique is used to model a set of single output functions with dimensionality ranging from fifteen to fifty independent variables and modality ranging from one to ten local maxima. The number of points used to train the models is increased until a predetermined error threshold is met. Results show that kriging metamodels perform most consistently across a variety of functions, although radial basis functions and support vector regression are very competitive for highly multimodal functions and functions with large local gradients, respectively. Support vector regression metamodels consistently offer the shortest build and prediction times when applied to large scale multimodal problems. © 2012 Taylor & Francis.
AUTHOR KEYWORDS: kriging; metamodel; radial basis function; support vector regression
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DOCUMENT TYPE: Article
SOURCE: Scopus
Rahmat-Samii, Y., Kovitz, J.M., Rajagopalan, H.
Nature-inspired optimization techniques in communication antenna designs
(2012) Proceedings of the IEEE, 100 (7), art. no. 6204306, pp. 2132-2144.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862688037&partnerID=40&md5=4542a3efa7e42f8e0ff06c13d4b22c94
AFFILIATIONS: Electrical Engineering Department, University of California Los Angeles, Los Angeles, CA 90095, United States
ABSTRACT: This paper summarizes the primary features inherent in current optimization methods typically applied to antenna designs and demonstrates their effectiveness by applying particle swarm optimization (PSO), a nature-inspired global optimization technique, to novel antenna design solutions in wireless communications. The concept of the PSO technique is briefly introduced and an outline of the important parameters that are utilized is summarized. Next, an implementation strategy combining PSO with numerical algorithms for electromagnetic solutions, namely the finite element method (FEM) and the method of moments (MoM), is discussed. In both realizations (PSO-FEM and PSO-MoM), the PSO technique drives the design variables, such as the antenna dimensions, geometrical features, etc., and the full-wave electromagnetic analysis engines evaluate the fitness function for the optimizer. Optimized antenna designs including a multiband handset antenna and an E-shaped patch antenna for circularly polarized (CP) applications are presented. Measurement results of prototype optimized designs are shown to demonstrate the functionality and effectiveness of the methodologies presented in this paper. © 2012 IEEE.
AUTHOR KEYWORDS: Antenna design; E-shaped; multiband handset antenna; nature inspired; optimization; particle swarm; wireless communications
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Liu, Y.L.a b , Liu, D.F.a b , Liu, Y.F.a b , He, J.H.a b , Jiao, L.M.a b , Chen, Y.Y.a b , Hong, X.F.a
Rural land use spatial allocation in the semiarid loess hilly area in China: Using a Particle Swarm Optimization model equipped with multi-objective optimization techniques
(2012) Science China Earth Sciences, 55 (7), pp. 1166-1177.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863319293&partnerID=40&md5=fd6f28b0994845860ffe5f4b0d95a9a9
AFFILIATIONS: School of Resource and Environment Science, Wuhan University, Wuhan 430079, China;
Ministry of Education Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China
ABSTRACT: Semiarid loess hilly areas in China are enduring a series of environmental conflicts between urban expansion, cultivated land conservation, soil erosion and water shortage, and require land use allocation to reconcile these environmental conflicts. We argue that the optimized spatial allocation of rural land use can be achieved by a Particle Swarm Optimization (PSO) model in conjunction with multi-objective optimization techniques. Our study focuses on Yuzhong County of Gangsu Province in China, a typical catchment on the Loess Plateau, and proposes a land use spatial optimization model. The model maximizes land use suitability and spatial compactness based on a variety of constraints, e. g. optimal land use structure and restrictive areas, and employs an improved PSO algorithm equipped with a determinant initialization method and a dynamic weighted aggregation (DWA) method to obtain the optimized land use spatial pattern. The results suggest that (1) approximately 4% of land use should be reallocated and these changes would alleviate the environmental conflicts in the study area; (2) the major reshuffling is slope farmland and newly added construction and cultivated land, whereas the unchanged areas are largely forests and basic farmland; and (3) the PSO is capable of optimizing rural land use allocation, and the determinant initialization method and DWA can improve the performance of the PSO. © 2012 Science China Press and Springer-Verlag Berlin Heidelberg.
AUTHOR KEYWORDS: Loess Plateau; multi-objective optimization; particle swarm optimization; rural land use; spatial allocation
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DOCUMENT TYPE: Article
SOURCE: Scopus
Dong, Z.
Using group-decided Watts-Strogatz particle swarm optimisation to direct orbits of chaotic systems
(2012) International Journal of Wireless and Mobile Computing, 5 (3), pp. 244-248.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863965751&partnerID=40&md5=d17399445777c4750973da5508905fc1
AFFILIATIONS: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi 030024, China
ABSTRACT: Group-decided Watts-Strogatz Particle Swarm Optimisation (GWSPSO) is a new novel variant of Particle Swarm Optimisation (PSO) that aims to enhance the escaping capability from local optimum by incorporating group decision mechanism and Watts-Strogatz small-world topology. In this paper, GWSPSO is employed to solve the directing orbits of chaotic systems, simulation results show this new variant increases the performance significantly when compared with the standard version of PSO. Copyright © 2012 Inderscience Enterprises Ltd.
AUTHOR KEYWORDS: Group decision mechanism; Orbits of chaotic systems; Watts-Strogatz small-world topology
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DOCUMENT TYPE: Article
SOURCE: Scopus
Iacca, G., Neri, F., Mininno, E.
Noise analysis compact differential evolution
(2012) International Journal of Systems Science, 43 (7), pp. 1248-1267.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861829870&partnerID=40&md5=23ca33c30005c49b2724b32fb3e20403
AFFILIATIONS: Department of Mathematical Information Technology, University of Jyvskyl, P.O. Box 35, Agora, Finland
ABSTRACT: This article proposes a compact algorithm for optimisation in noisy environments. This algorithm has a compact structure and employs differential evolution search logic. Since it is a compact algorithm, it does not store a population of solutions but a probabilistic representation of the population. This kind of algorithmic structure can be implemented in those real-world problems characterized by memory limitations. The degree of randomization contained in the compact structure allows a robust behaviour in the presence of noise. In addition the proposed algorithm employs the noise analysis survivor selection scheme. This scheme performs an analysis of the noise and automatically performs a re-sampling of the solutions in order to ensure both reliable pairwise comparisons and a minimal cost in terms of fitness evaluations. The noise analysis component can be reliably used in noise environments affected by Gaussian noise which allow an a priori analysis of the noise features. This situation is typical of problems where the fitness is computed by means of measurement devices. An extensive comparative analysis including four different noise levels has been included. Numerical results show that the proposed algorithm displays a very good performance since it regularly succeeds at handling diverse fitness landscapes characterized by diverse noise amplitudes. © 2012 Taylor & Francis.
AUTHOR KEYWORDS: compact differential evolution; differential evolution; noise analysis selection scheme; noisy fitness landscape
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DOCUMENT TYPE: Article
SOURCE: Scopus
Benxian, Y.U.E.a , Hongbo, L.I.U.a , Abraham, A.b
Dynamic trajectory and convergence analysis of swarm algorithm
(2012) Computing and Informatics, 31 (2), pp. 371-392.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862554887&partnerID=40&md5=6c614399e7cf1f8d6c23909cdb69ac73
AFFILIATIONS: School of Electronic and Information Engineering, Dalian University of Technology, Dalian 116023, China;
Machine Intelligence Research Labs, MIR Labs, Auburn, WA 98071-2259, United States
ABSTRACT: Swarm Intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. Although the swarm algorithms have exhibited good performance across a wide range of application problems, it is difficult to analyze the convergence. In this paper, we discuss the dynamic trajectory and convergence of the swarm intelligent model, namely the particle swarm algorithm. We explore the tradeoff between exploration and exploitation using differential analysis and Laplace transform. The trajectories are parsed into first-order inertial element and second-order oscillation element. Their transfer functions are derived, and the trajectories are described in explicit time functions. The first-order inertial element is helpful to maintain the trajectory's stability and algorithm convergence, while the second-order oscillation element trends to explore some new search spaces for the better solutions. The convergence regions of the swarm system are analyzed using the spectral radius and Lyapunov second theorem on stability.
AUTHOR KEYWORDS: Convergence; Stability; Swarm algorithm; Swarm intelligence
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DOCUMENT TYPE: Article
SOURCE: Scopus
Shao, Y.a b , Yao, X.b , Tian, L.c , Chen, H.d
A multiswarm optimizer for distributed decision making in virtual enterprise risk management
(2012) Discrete Dynamics in Nature and Society, 2012, art. no. 904815, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862289550&partnerID=40&md5=a3d91e6cbd301170d258ccb8053f4f94
AFFILIATIONS: College of Information Science and Engineering, Shenyang University, Shenyang 110044, China;
School of New Energy Engineering, Shenyang University of Technology, Shenyang 110036, China;
Science and Technology Agency, Shenyang University, Shenyang 110036, China;
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
ABSTRACT: We develop an optimization model for risk management in a virtual enterprise environment based on a novel multiswarm particle swarm optimizer called PS
2O. The main idea of PS
2O is to extend the single population PSO to the interacting multiswarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multiswarm to converge to the global optimum. With five mathematical benchmark functions, PS
2O is proved to have considerable potential for solving complex optimization problems. PS
2O is then applied to risk management in a virtual enterprise environment. Simulation results demonstrate that the PS
2O algorithm is more feasible and efficient than the PSO algorithm in solving this real-world problem. Copyright © 2012 Yichuan Shao et al.
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DOCUMENT TYPE: Article
SOURCE: Scopus
Prasad, J., Souradeep, T.
Cosmological parameter estimation using particle swarm optimization
(2012) Physical Review D - Particles, Fields, Gravitation and Cosmology, 85 (12), art. no. 123008, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862743105&partnerID=40&md5=d1d399dd21609acf18c309f9497c1431
AFFILIATIONS: IUCAA, Post Bag 4, Ganeshkhind, Pune 411007, India
ABSTRACT: Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point. © 2012 American Physical Society.
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DOCUMENT TYPE: Article
SOURCE: Scopus
Abe, A., Nemoto, S.
An energy saving feedforward control technique for a 2-DOF flexible manipulator
(2012) Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, 78 (789), pp. 1325-1337.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862201994&partnerID=40&md5=741c1d6f76a510df9d269b063127b08c
AFFILIATIONS: Department of Systems, Control and Information Engineering, Asahikawa National College of Technology, 2-2-1-6 Syunkodai, Asahikawa, Hokkaido 071-8142, Japan
ABSTRACT: This paper investigates a feedforward control technique for saving the operating energy of a 2-DOF flexible manipulator with a point-to-point (PTP) motion, in which the residual vibration also can be suppressed. The 2-DOF manipulator has one prismatic joint and one revolute joint. The Lagrangian approach in conjunction with the assumed modes method is applied to derive the equations of motion of the manipulator system. For the PTP motion task, the trajectory of the translational motion is set to a cycloidal motion. On the other hand, the trajectory of the rotational motion is designed to simultaneously minimize the residual vibration and the operating energy. In the present method, we attempt to express the trajectory of the joint angle by an artificial neural network (ANN), and then a vector evaluated particle swarm optimization (VEPSO) algorithm, which is a multi-objective optimization algorithm, is used for learning the ANN. By operating the manipulator along the trajectory obtained by the proposed method, the residual vibrations can be suppressed under the minimum energy condition. The numerical simulation results are compared with the experimental results; this comparison reveals the applicability and effectiveness of the proposed method. © 2012 The Japan Society of Mechanical Engineers.
AUTHOR KEYWORDS: Flexible Manipulator; Motion Control; Neural Networks; Positioning; Vector Evaluated Particle Swarm Optimization; Vibration Control
REFERENCES: Benosman, M., Vey, L.G., Control of flexible manipulators: A survey (2004) Robotica, 22 (5), pp. 535-545;
Dwivedy, S.K., Eberhard, P., Dynamic Analysis of Flexible Manipulators: A Literature;
Kijima, H., Hiruma, T., Evolutionary Learning Acquisition of Optimal Joint Angle;
Parsopoulos, K.E., Tasoulis, D.K., Vrahatis, M.N., Multiobjective optimization using parallel vector evaluated particle swarm optimization (2004) Proceedings of the IASTED International Conference. Applied Informatics, pp. 823-828., 411-177, Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics);
Abe, A., Komuro, K., An energy saving open-loop control technique for flexible manipulators (2011) Proceedings of the 2011 IEEE International Conference on Mechatronics and Automation, pp. 416-421., Beijing, China;
Abe, A., Trajectory planning for flexible cartesian robot manipulator by using artificial neural network: Numerical simulation and experimental verification (2011) Robotica, 29 (5), pp. 797-804
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Chen, J.a , Yang, D.a , Feng, Z.b
A novel quantum particle swarm optimizer with dynamic adaptation
(2012) Journal of Computational Information Systems, 8 (12), pp. 5203-5210.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863215204&partnerID=40&md5=3cac89863c3dd41226785eef3b323769
AFFILIATIONS: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China;
College of Zhijiang, Zhejiang University of Technology, Hangzhou 310024, China
ABSTRACT: Quantum-behaved particle swarm (QPSO) is a global-convergence-guaranteed algorithm and has a better search ability than the traditional particle swarm optimizer(PSO). However, like other evolutionary optimization algorithms, premature and population diversity lose is inevitable, especially for solving constrained optimizer problems. In this paper, a novel quantum particle swarm optimizer combined with dynamic adaptation (DQPSO) is brought up. Before describing the new method, we firstly introduce the origin and development of PSO and QPSO. Penalty function mechanism is carried out and adopted for DQPSO solving constrained problems. And DQPSO is testified by several typical benchmark functions and the experiment results prove that the DQPSO with improved J penalty mechanism outperforms PSO and QPSO in above cases. © 2012 Binary Information Press.
AUTHOR KEYWORDS: Constrained optimization; J penalty function; Premature convergence; Quantum particle swarm optimizer with dynamic adaptation
REFERENCES: Kendall, G., Su, Y., A particle swarm optimization approach in the construction of optimal risky portfolios (2005) Proceedings of the 23rd IASTED International Multi-Conference Artificial Intelligence and Applications, pp. 324-344., Innsbruck, Austria, Feb 14-16;
Zielinski, K., Laur, R., Constrained single-objective optimization using particle swarm optimization (2006) IEEE Congress on Evolutionary Computation, pp. 23-39., July 16-21;
Kennedy, J.F., Eberhart, R.C., Shi, Y., (2001) Swarm Intelligence, , San Francisco (USA): Morgan Kaufmann Pub;
Kennedy, J.F., Bare bones particle swarms (2003) Proceedings of the IEEE Swarm Intelligence Symposium, pp. 80-97., Indianapolis (IN);
Krohling, R.A., Gaussian swarm: A novel particle swarm optimization algorithm (2004) Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems (CIS), pp. 372-376., Singapore;
Krohling, R.A., Coelho, L.S., PSO-E: Particle swarm with exponential distribution (2006) IEEE World Congress on Computational Intelligence, Proceedings of IEEE Congress on Evolutionary Computation Congress on Evolutionary Computation, pp. 5577-5582., Vancouver (Canada);
dos Coelho, L.S., A quantum particle swarm optimizer with chaotic mutation operator Chaos (2008) Solitons and Fractals, 37, pp. 1409-1418;
Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method for constrained optimization problems (2002) Proceedings of the 2002 Euro-International Symposium on Computation Intelligence, pp. 214-220;
Secrest, B.R., Lamont, G.B., Visualizing particle swarm optimization-Gaussian particle swarm optimization (2003) Proceedings of the IEEE Swarm Intelligence Symposium, pp. 198-204., Indianapolis (IN, USA)
DOCUMENT TYPE: Article
SOURCE: Scopus
Ibrahim, Z.a , Khalid, N.K.a , Mukred, J.A.A.a , Buyamin, S.a , Yusof, Z.M.a , Faiz, M.a , Saaid, M.b , Mokhtar, N.c , Engelbrecht, A.P.d
A DNA sequence design for DNA computation based on binary vector evaluated particle swarm optimization
(2012) International Journal of Unconventional Computing, 8 (2), pp. 119-137.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862146760&partnerID=40&md5=ccad1c23344f1c3e9a4431a4c1644f71
AFFILIATIONS: Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia;
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia;
Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Malaysia;
Department of Computer Science, University of Pretoria, South Africa
ABSTRACT: Deoxyribonucleic Acid (DNA) has certain unique properties such as selfassembly and self-complementary in hybridization, which are important in many DNA-based technologies. DNA computing, for example, uses these properties to realize a computation, in vitro, which consists of several chemical reactions. Other DNA-based technologies such as DNAbased nanotechnology and polymerase chain reaction also depend on hybridization to assemble nanostructure and to amplify DNA templates, respectively. Hybridization of DNA can be controlled by properly designing DNA sequences. In this study, sequences are designed such that each sequence uniquely hybridizes to its complementary sequence, but not to any other sequences. This objective can be formulated using four objective functions, namely, similarity, H
measure, continuity, and hairpin. Binary vector evaluated particle swarm optimization (Binary VEPSO) is employed to solve the DNA sequence design problem by minimizing the objective functions subjected to two constraints: melting temperature and GC
content. Several set of good sequences are produced, which are better than other research works where only a set of sequences is generated. © 2012 Old City Publishing, Inc.
AUTHOR KEYWORDS: Binary particle swarm optimization; DNA sequence design; Multi objective optimization; Vector evaluated PSO
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Reece, R.J., (2004) Analysis of Genes and Genomes, , England: Wiley;
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Shin, S.Y., Lee, I.H., Kim, D., Zhang, B.T., Multi-objective evolutionary optimization of DNA sequences for reliable DNA computing (2005) IEEE Transaction on Evolutionary Computation, 9 (2), pp. 143-158;
Guangzhao, C., Yunyun, N., Yangfeng, W., Xuncai, Z., Linqiang, P., A New approach Based on PSO algorithm to Find Good Computational Encoding Sequences (2007) Progress in Natural Science, 17 (6), pp. 712-716;
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Omkar, S.N., Mudigere, D., Naik, G.N., Gopalakrishnan, S., Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures (2007) Computers and Structures, 86 (1-2), pp. 1-14;
Vlachogiannis, J.G., Lee, K.Y., Reactive Power Control Based on Particle Swarm Multi-Objective Optimization (2005) Proc. of 13 th International Conference on Intelligent Systems Application to Power Systems, , In, Arlington, VA, November 6-10, 2005;
Vlachogiannis, J.G., Lee, K.Y., Determining generator contributions to transmission system using parallel vector evaluated particle swarm optimization Power Systems (2005) IEEE Transactions, 20 (4), pp. 1765-1774;
Parsopoulos, K.E., Vrahatis, M.N., On the Computation of All Global Minimisers through Particle Swarm Optimization (2004) IEEE Transactions on Evolutionary Computation, 8 (3), pp. 211-224;
Plagianakos, V.P., Vrahatis, M.N., Parallel evolutionary training algorithms for "hardware-friendly" neural networks (2002) Natural Computing, 1 (2-3), pp. 307-322;
Eberhart, R.C., Shi, Y., Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization (2000) Proceedings of IEEE congress evolutionary computation, pp. 84-88., San Diego, CA;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Yan, C., Liu, C., Wang, J.
Application of a new hybrid particle swarm optimization in the optimal design of nuclear power components
(2012) Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 33 (4), pp. 534-538.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861936887&partnerID=40&md5=9bf697cfbf71750c2b300b1613fd72b5
AFFILIATIONS: College of Nuclear Science and Technology, Harbin Engineering University, Harbin 150001, China
ABSTRACT: The standard particle swarm optimization has the shortcomings of slow convergence speed and poor accuracy of convergence, while easily falling into the local optimum when dealing with nonlinear constraint optimization problems. To overcome these difficulties, a new kind of hybrid particle swarm optimization algorithm was designed; it adopts the feasibility principle to handle constraint conditions, avoiding the difficulty of choosing a punishment factor when using the penalty function method. The basic complex algorithm was introduced to the hybrid particle swarm optimization algorithm to produce an initial feasible group, accelerating particle swarm convergence speed. The crossover and mutation strategy in a genetic algorithm was introduced to keep the particle swarm from falling into the local optimum. An improved complex algorithm was employed for obtaining better results when achieving iteration times in order to improve the accuracy of the optimal result. Testing the benchmark function through the optimization calculation shows that the new hybrid particle swarm optimization algorithm has better optimization performance, and it has been satisfactorily applied in the optimal design of nuclear power components.
AUTHOR KEYWORDS: Complex algorithm; Genetic algorithm; Nuclear power components; Optimal design; Particle swarm optimization
REFERENCES: Kennedy, J., Eberhart, R.C., Particle swarm optimization (1995) IEEE International Conference on Neural Networks, pp. 1942-1948., Perth, Australia;
Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method for constrained optimization problems (2002) Artificial Intelligence, 3, pp. 603-607;
Deb, K., An efficient constraint handing method for genetic algorithms (2000) Computer Methods in Applied Mechanics and Engineering, 186 (2), pp. 311-338;
Jiao, W., Liu, G., Zhang, Y., Particle swarm optimization based on simulated annealing for solving constrained optimization problems (2010) Systems Engineering and Electronics, 32 (7), pp. 1532-1536;
Gu, H., Xu, L., Perceptive particle swarm optimization algorithm for constrained optimization problems (2011) Journal of Computer Applications, 31 (1), pp. 85-88;
Vieira, D.A.G., Adriano, R.L.S., Krähenbüwl, L., Handling constraints as objectives in a multi-objective genetic based algorithm (2002) Journal of Microwaves and Optoelectronics, 2 (6), pp. 50-58;
Wang, F., Wu, C., Yang, H., Study on the productive method on the initial population by using genetic algorithm to solve the constrained optimization problem (2004) Journal of Northeast Agricultural University, 35 (5), pp. 608-611;
Lovbjerg, M., Rasmussen, T.K., Krink, T., Hybrid particle swarm optimization with breeding and subpopulation (2000) IEEE International Conference on Evolutionary Computation, pp. 1-6., San Diego, USA;
Higashi, N., Iba, H., Particle swarm optimization with Gaussian mutation (2003) IEEE Proc of the IEEE Swarm Intelligence Symp, pp. 72-79., Indianapolis, USA;
Wang, M., Wang, J., He, S., An approach for solving complex local optimum and accelerating calculation (2011) Computer Applications and Software, 28 (2), pp. 277-278;
Runarsson, T.P., Yao, X., Ranking for constrained evolutionary optimization (2000) IEEE Transactions on Evolutionary Computation, 4 (3), pp. 284-294;
Coath, G., Halgamuge, S.K., A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems (2003) IEEE 2003 Congress on Evolutionary Computation, pp. 2419-2425., Ganberra, Australia;
Pulido, G.T., Coello, C.A.C., A constraint-handling mechanism for particle swarm optimization (2004) IEEE 2004 Congress on Evolutionary Computation, pp. 1396-1403., Portland, USA;
Ma, R., Liu, Y., Qin, Z., Momentum particle swarm optimizer for constrained optimization (2010) Journal of System Simulation, 22 (11), pp. 2485-2488;
Qin, H., Yan, C., Wang, J., Optimal design of vertical circulation steam generator weight (2011) Atomic Energy Science and Technology, 45 (1), pp. 66-72;
Zheng, J., Yan, C., Wang, J., Optimal design of condenser volume in nuclear power plant (2011) Atomic Energy Science and Technology, 45 (1), pp. 60-65
DOCUMENT TYPE: Article
SOURCE: Scopus
Dalton, S.K., Farajpour, I., Juang, C.H., Atamturktur, S.
Robust design optimization to account for uncertainty in the structural design process
(2012) Conference Proceedings of the Society for Experimental Mechanics Series, 1, pp. 341-351.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861739657&partnerID=40&md5=b5716de79ea31591930470af4d813ced
AFFILIATIONS: Clemson University, Clemson, SC 29634, United States
ABSTRACT: Structural systems are subject to inherent uncertainties due to the variability in many hard-to-control 'noise factors' including but not limited to external loads, material properties, and construction workmanship. Two design methodologies were developed to quantify the variability associated with the design process: Allowable Stress Design (ASD) and Load and Resistance Factor Design (LRFD). These traditional approaches explicitly recognize the presence of uncertainty, however they do not take robustness against this uncertainty into consideration. Overlooking robustness against uncertainty in the structural design process has two main problems. First, the design may not satisfy the safety requirements if the actual uncertainties in the noise factors are underestimated. Thus, the safety requirements can easily be violated because of the high variation of the system response due to noise factors. Second, to guarantee safety in the presence of this high variability of the system response, the structural designer may be forced to choose an overly conservative, inefficient and thus costly design. When the robustness against uncertainty is not treated as one of the design objectives, this trade-off between the over-design for safety and the under-design for cost-savings is exacerbated. This paper demonstrates that safe and cost-effective designs are achievable by implementing Robust Design concepts originally developed in manufacturing engineering to proactively consider the robustness against uncertainty as one of the design objectives. Robust Design concepts can be used to formulate structural designs which are insensitive to inherent variability in the design process, thus save cost, and exceed the main objectives of user safety and serviceability. This paper presents two methodologies for the application of Robust Design principles to structural design utilizing two optimization schemes: one-at-a-time optimization method and Particle Swarm Optimization (PSO) method. © The Society for Experimental Mechanics, Inc. 2012.
AUTHOR KEYWORDS: Optimization; Robust design; Structural modeling and design; Uncertainty quantification in structural design
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Ahn, C.W.a , An, J.b , Yoo, J.-C.a
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs
(2012) Information Sciences, 192, pp. 109-119. Cited 2 times.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857864609&partnerID=40&md5=d204a52f564c4498fc7b62dfd6b60737
AFFILIATIONS: School of Information and Communication Engineering, Sungkyunkwan University, South Korea;
Daegu Gyeongbuk Institute of Science and Technology (DGIST), South Korea
ABSTRACT: This paper presents a novel framework of the estimation of particle swarm distribution algorithms (EPSDAs). The aim is to effectively combine particle swarm optimization (PSO) with the estimation of distribution algorithms (EDAs) without losing their unique features. This aim is achieved by incorporating the following mechanisms: (1) selection is applied to the local best solutions in order to obtain more promising individuals for model building, (2) a probabilistic model of the problem is built from the selected solutions, and (3) new individuals are generated by a stochastic combination of the EDA's model sampling method and the PSO's particle moving mechanism. To exhibit the utility of the EPSDA framework, an extended compact particle swarm optimization (EcPSO) is developed by combining the strengths of the extended compact genetic algorithm (EcGA) with binary PSO (BPSO), along the lines of the suggested framework. Due to its effective nature of harmonizing the global search of EcGA with the local search of BPSO, EcPSO is able to discover the optimal solution in a fast and reliable manner. Experimental results on artificial to real-world problems have adduced grounds for the effectiveness of the proposed approach. © 2010 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Estimation of distribution algorithms; Extended compact genetic algorithm; Global search; Local search; Particle swarm optimization; Probabilistic model building
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DOCUMENT TYPE: Article
SOURCE: Scopus
Neshat, M.a , Sargolzaei, M.b , Masoumi, A.c , Najaran, A.a
A New kind of PSO: Predator particle swarm optimization
(2012) International Journal on Smart Sensing and Intelligent Systems, 5 (2), pp. 521-539.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864557931&partnerID=40&md5=b3f5e39132cfd46bbbaf28d7350b638d
AFFILIATIONS: Department of Computer Science, Shirvan Branch, Islamic Azad University, Shirvan, Iran;
Department of Software Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran;
Department of Hardware Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran
ABSTRACT: Today, swarm intelligence is widely used in optimization problems. PSO is one the best swarm intelligence methods. In the method, each particle moves toward the direction in which the best individual and group experience has happened. The most important disadvantage of this method is that it falls in local optima. To fix the problem, a metaheuristic method is proposed in this paper. There has always been a competition between prey and predator in the nature. Little birds often fly in a colony form to run away from birds of prey. Being inspired by the phenomenon, a new particle is added to PSO algorithm known as predator, also a new behavior called Take flight from predator" is defined. This particle is responsible for attacking the colony of particles so as to prevent the premature convergence. With the predator attack to the colony, particles run away and again the chance rises for a Global optimum to be gained. The attack just caused particles dispersion and no particle dies. It can be repeated for m times and the optimal point is saved each time. To test the method, 12 benchmark functions were employed and the results were compared to OPSO, VPSO, LPSO, and GPSO methods. Regarding the results, the proposed method had a better performance.
AUTHOR KEYWORDS: Local optimum; Particle swarm optimization; Predator; Premature convergence.
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DOCUMENT TYPE: Article
SOURCE: Scopus
Gao, H., Cao, J., Zhao, Y.
Membrane quantum particle swarm optimisation for cognitive radio spectrum allocation
(2012) International Journal of Computer Applications in Technology, 43 (4), pp. 359-365.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861839438&partnerID=40&md5=d24a385989090530c3e9a16581571898
AFFILIATIONS: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
ABSTRACT: To design a novel intelligence algorithm for spectrum allocation problem, a membrane quantum particle swarm optimisation (MQPSO) is proposed. The proposed MQPSO algorithm applies the theory of membrane computing to quantum particle swarm optimisation (QPSO), which is an effective discrete optimisation algorithm. Then the proposed MQPSO algorithm is used to solve spectrum allocation problems of cognitive radio system. By hybridising the QPSO and membrane theory, the quantum state and measure state of the quantum particle can be well evolved in membrane structure. The new spectrum allocation algorithm can search global optimal solution. Simulation results for cognitive radio system are provided to show that the designed spectrum allocation method is superior to some previous spectrum allocation algorithms. Copyright © 2012 Inderscience Enterprises Ltd.
AUTHOR KEYWORDS: Cognitive radio; Membrane computing; P system; QPSO; Quantum particle swarm optimisation; Spectrum allocation
REFERENCES: Akyildiz, I.F., Lee, W.-Y., Vuran, M.C., Mohanty, S., NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey (2006) Computer Networks, 50 (13), pp. 2127-2159., DOI 10.1016/j.comnet.2006.05.001, PII S1389128606001009;
Cao, L., Zheng, H., Distributed spectrum allocation via local bargaining (2005) 2005 Second Annual IEEE Communications Society Conference on Sensor and AdHoc Communications and Networks, SECON 2005, 2005, pp. 475-486., DOI 10.1109/SAHCN.2005.1557100, 1557100, 2005 Second Annual IEEE Communications Society Conference on Sensor and AdHoc Communications and Networks, SECON 2005;
Gao, H.Y., Diao, M., Quantum particle swarm optimization for MC-CDMA multiuser detection (2009) 2009 International Conference on Artificial Intelligence and Computational Intelligence, 2, pp. 132-136;
Haykin, S., Cognitive radio: Brain-empowered wireless communications (2005) IEEE Journal on Selected Areas in Communications, 23 (2), pp. 201-220., DOI 10.1109/JSAC.2004.839380;
Holland, J.H., (1975) Adaptation in Natural and Artificial Systems, , University of Michigan Press, Ann Arbor, MI;
Huang, J., Berry, R.A., Honig, M.L., Auction-based spectrum sharing (2006) Mobile Networks and Applications, 11 (3), pp. 405-418., DOI 10.1007/s11036-006-5192-y, Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks;
Kennedy, J., Eberhart, R., A discrete binary version of the particle swarm optimization (1997) Proc. IEEE International Conference on Systems, Man, and Cybernetics, pp. 4104-4108;
Kloeck, C., Jaekel, H., Jondral, F.K., Dynamic and local combined pricing, allocation and billing system with cognitive radios (2005) 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2005, pp. 73-81., DOI 10.1109/DYSPAN.2005.1542619, 1542619, 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2005;
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Parsopoulos, K.E., Kariotou, F., Dassios, G., Vrahatis, M.N., Tackling magnetoencephalography with particle swarm optimization algorithm with fine tuning operators (2009) International Journal of Bio-inspired Computation, 1 (1-2), pp. 32-49;
Pǎun, G., Further twenty-six open problems in membrane computing (2005) Proc. 3rd Brainstorming Meeting on Membrane Computing, pp. 249-262., Sevilla, Spain;
Pun, G., Rozenberg, G., A guide to membrane computing (2002) Theoretical Computer Science, 287 (1), pp. 73-100., DOI 10.1016/S0304-3975(02)00136-6, PII S0304397502001366, Natural Computing;
Peng, C., Zheng, H., Zhao, B.Y., Utilization and fairness in spectrum assignment for opportunistic spectrum access (2006) Mobile Networks and Applications, 11 (4), pp. 555-576., DOI 10.1007/s11036-006-7322-y;
Ramana Murthy, G., Senthil Arumugam, M., Loo, C.K., Hybrid particle swarm optimization algorithm with fine tuning operators (2009) International Journal of Bio-inspired Computation, 1 (1-2), pp. 59-64;
Yuan, D.L., Chen, Q., Particle swarm optimisation algorithm with forgetting character (2010) International Journal of Bio-inspired Computation, 2 (1), pp. 14-31;
Zhang, G.X., A quantum-inspired evolutionary algorithm based on P systems for knapsack problem (2008) Fundamenta Informaticae, 87 (1), pp. 93-116;
Zhao, Z.J., Peng, Z., Zheng, S.L., Shang, J.N., Cognitive radio spectrum allocation using evolutionary algorithms (2009) IEEE Transactions on Wireless Communications, 8 (9), pp. 4421-4425., September;
Zhao, Z.J., Peng, Z., Zheng, S.L., Xu, S.Y., Lou, C.Y., Yang, X.N., Cognitive radio spectrum assignment based on quantum genetic algorithms (2009) Acta Physica Sinica, 52 (2), pp. 1358-1363;
Zheng, H., Peng, C., Collaboration and fairness in opportunistic spectrum access (2005) IEEE International Conference on Communications, 5, pp. 3132-3136., WN10-1, ICC 2005 - 2005 IEEE International Conference on Communications
DOCUMENT TYPE: Article
SOURCE: Scopus
Gardner, M., McNabb, A., Seppi, K.
A speculative approach to parallelization in particle swarm optimization
(2012) Swarm Intelligence, 6 (2), pp. 77-116.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861233619&partnerID=40&md5=5d77ae893d7f530fef87b313c1d3939f
AFFILIATIONS: Brigham Young University, 3361 TMCB, Provo, UT 84604, United States
ABSTRACT: Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In these approaches, the only benefit of additional processors is an increased swarm size. However, in many cases this is not efficient when scaled to very large swarm sizes (on very large clusters). Current methods cannot answer well the question: "How can 1000 processors be fully utilized when 50 or 100 particles is the most efficient swarm size?" In this paper we attempt to answer that question with a speculative approach to the parallelization of PSO that we refer to as SEPSO. In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm. SEPSO produces results that are exactly equivalent to PSO; that is, SEPSO is a new method of parallelization and not a new PSO algorithm or variant. However, given this new parallelization model, we can relax the requirement of exactly reproducing PSO in an attempt to produce better results. We present several such relaxations, including keeping the best speculative position evaluated instead of the one corresponding to the standard behavior of PSO, and speculating several iterations ahead instead of just one. We show that these methods dramatically improve the performance of parallel PSO in many cases, giving speed ups of up to six compared to previous parallelization techniques. © 2011 Springer Science + Business Media, LLC.
AUTHOR KEYWORDS: Optimization methods; Parallel algorithms; Particle swarm optimization; Speculative decomposition
REFERENCES: Belal, M., El-Ghazawi, T., Parallel models for particle swarm optimizers (2004) International Journal of Intelligent Computing and Information Sciences, 4 (1), pp. 100-111;
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Scriven, I., Ireland, D., Lewis, A., Mostaghim, S., Branke, J., Asynchronous multiple objective particle swarm optimisation in unreliable distributed environments (2008) Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2481-2486., Piscataway: IEEE Press;
Scriven, I., Lewis, A., Ireland, D., Lu, J., Decentralised distributed multiple objective particle swarm optimisation using peer to peer networks (2008) Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2925-2928., Piscataway: IEEE Press;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Ali, L.a , Sabat, S.L.a , Udgata, S.K.b
Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems
(2012) International Journal of Bio-Inspired Computation, 4 (3), pp. 155-166.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862184154&partnerID=40&md5=820bb971c6bcd371c5d6b89120b34399
AFFILIATIONS: School of Physics, University of Hyderabad, Hyderabad - 500046, India;
Department of Computer and Information Science, University of Hyderabad, Hyderabad - 500046, India
ABSTRACT: Most of the real world science and engineering optimisation problems are non-linear and constrained. This paper presents a hybrid algorithm by integrating particle swarm optimisation with stochastic ranking for solving standard constrained numerical and engineering benchmark problems. Stochastic ranking technique that uses bubble sort mechanism for ranking the solutions and maintains a balance between the objective and the penalty function. The faster convergence of particle swarm optimisation and the ranking technique are the major motivations for hybridising these two concepts and to propose the stochastic ranking particle swarm optimisation (SRPSO) technique. In this paper, SRPSO is used to optimise 15 continuous constrained single objective benchmark functions and five well-studied engineering design problems. The performance of the proposed algorithm is evaluated based on the statistical parameters such mean, median, best, worst values and standard deviations. The SRPSO algorithm is compared with six recent algorithms for function optimisation. The simulation results indicate that the SRPSO algorithm performs much better while solving all the five standard engineering design problems where as it gives a competitive result for constrained numerical benchmark functions. © 2012 Inderscience Enterprises Ltd.
AUTHOR KEYWORDS: Constrained optimisation; Particle swarm optimisation; PSO; SR; Stochastic ranking
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wei, B.a b , Li, Y.a b , Yu, F.c , He, G.b
A novel difference-based particle swarm optimization algorithm for multimodal function optimization
(2012) Journal of Convergence Information Technology, 7 (10), pp. 166-174.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863105824&partnerID=40&md5=de8db0d8df4a239c3a1cb0661b37fd28
AFFILIATIONS: Computer School, Wuhan University, Wuhan, Hubei, China;
State Key Lab of Software Engineering, Wuhan University, Wuhan, Hubei, China;
Department of Physics and Electronics Information Engineering, Zhangzhou Normal University, Zhangzhou, China
ABSTRACT: In this paper, a novel difference-based particle swarm optimization algorithm (DBPSO) is presented. In DBPSO, each dimension of all particles can potentially learn from a different exemplar, or it is updated in the manner of difference-based strategy at a certain probability. This mechanism will enable the diversity of the swarm to be preserved to discourage premature convergence and act on the globally best particle to jump out of the likely local optima. The DBPSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. Results show that DBPSO substantially enhances the performance of the PSO when compared with seven other recent variants of the PSO.
AUTHOR KEYWORDS: Differential evolution; Particle swarm optimization; Premature convergent
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DOCUMENT TYPE: Article
SOURCE: Scopus
Feng, L.a , Wei, W.b
Research of PSO/genetic algorithms and development of its hybrid algorithm
(2012) International Journal of Digital Content Technology and its Applications, 6 (11), pp. 328-335.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863322708&partnerID=40&md5=6a81f0a5f57f5f040b6ffcf358ff6696
AFFILIATIONS: Department of Information Engineering, Shaanxi Polytechnic Institute, Shaanxi, Xian'yang, 712000, China;
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China
ABSTRACT: The basic theories, development and applications of particle swarm optimization and genetic algorithm are introduced- d. Some models of improved PSO algorithms are outlined. Characteristics of PSO and GA are compared. Two methods of hybrid of PSO and GA at present was summarized: hybrid with two algorithms entirely or with only a few steps, and illustrated with flowchart. Limitation of two methods of hybrid was analyzed. Pointed out that hybrid algorithms can be improved with a balance between speed and accuracy of computation. Finally, pointed out application of PSO needs to be extended, and hybrid with other algorithms is thought a good way to improve PSO algorithm.
AUTHOR KEYWORDS: Evolutionary algorithm; Genetic algorithm; Hybrid swarm intelligence; Particle swarm optimization
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Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimizer in noisy and continuously changing environments (2001) Proceedings of the IASTED International Conference On Artificial Intelligence and Soft Computing, pp. 289-294;
van den Bergh, F., (2002) An Analysis of Particle Swarm Optimizers, , Master's thesis of University of Pretoria, South Africa;
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Shi, Y.H., Experimental Study of Particle Swarm Optimization (2000) Proceedings of SCI Conference, , Orlando, FL;
Juang, C.-F., A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design (2004) Proceedings of IEEE TRANSACTIONS ON SYSTEMS, MAN, and CYBERNE- TICS-PART B: CYBERNETICS, 34 (2), pp. 997-1006., APRIL;
Shi, X.H., Wan, L.M., Lee, H.P., Yang, X.W., Wang L., M., Liang, Y.C., An improved genetic algorithm with variable population size and a PSO-GA based hybrid evolutionary algorithm (2003) Proceedings of the Second International Conference On Machine Learning and Cybernetics, , Wan, 2-5 November;
Shi, X.H., Lu, Y.H., Zhou, C.G., Lee, H.P., Lin, W.Z., Liang, Y.C., Hybrid evolutionary algorithms based on PSO and GA (2003) Journal for IEEE 0-7803-7804-0/03;
Shi, Y., Russell, C., Eberhart, Fuzzy Adaptive Particle Swarm Optimization Proceedings of the IEEE Congress On Evolutionary Computation (CEC 2001), p. 2001;
Wei, W., Zhou, B., Qi, Y., Features Detection Based on a Variational Model in Sensornets (2010) Journal of International Journal of Digital Content Technology and Its Applications, 4 (7), pp. 115-127;
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Wei, W., Jinlin, J., Many-facet Rasch Model's Application in the Evaluation of Test Validity (2011) International Journal of Digital Content Technology and Its Applications, 5 (11)
DOCUMENT TYPE: Article
SOURCE: Scopus
Wang, K.-K., Lü, Q., Zhao, H.-Q., Zhang, W.
Hybrid algorithm for solving complex constrained optimization problems based on PSO and ABC
(2012) Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 34 (6), pp. 1193-1199.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864534196&partnerID=40&md5=a7292b9a278326639c55c49f527eb38c
AFFILIATIONS: Academy of Armored Force Engineering, Beijing 100072, China
ABSTRACT: In order to improve the performance of particle swarm optimization (PSO) in complex constrained optimization problems, a hybrid method combining PSO and artificial bee colony (ABC) is proposed. A feasibility-based rule is used to solve constrained problems, and the particle swarm is divided into feasible subpopulation and infeasible subpopulation. Some PSO particles containing the information of better feasible solutions and smaller constraint violation infeasible solutions are selected as food sources for ABC algorithm, which can make up for the tournament selection operator being invalid when the optimum is close to the boundary of constraint conditions. And the tabu table is used to save the local optimization results so as to avoid PSO trapping into local optimum. The algorithm is validated using four well-studied benchmark problems, and the results indicate that the PSO-ABC algorithm can find out better optimum and has a stronger solidity.
AUTHOR KEYWORDS: Artificial bee colony (ABC); Complex constrained optimization; Feasibility-based rule; Particle swarm optimization (PSO); Tabu table
REFERENCES: Hu, X.H., Eberhart, R., Solving constrained nonlinear optimization problems with particle swarm optimization (2002) Proc. of the 6th World Multi-conference on Systematics, Cybernetics and Informatics, pp. 203-206;
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Parsopoulos, K.E., Vrahatis, M.N., Unified particle swarm optimization for solving constrained engineering optimization problems (2005) Lecture Notes in Computer Science, 3612 (7), pp. 582-591;
He, Q., Wang, L., A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization (2007) Applied Mathematics and Computation, 186 (2), pp. 1407-1422;
Wang, Y., Cai, Z.X., Zhou, Y.R., Research and development of constrained optimization evolutionary algorithms (2009) Journal of Software, 20 (1), pp. 2691-2709;
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Wang, Y., Cai, Z.X., Zhou, Y.R., An adaptive tradeoff model for constrained evolutionary optimization (2008) IEEE Trans. on Evolutionary Computation, 12 (1), pp. 80-92;
Mezura-Montes, E., Coello, C.A.C., A survey of constraint-handing techniques based on evolutionary multi-objective optimization (2006), Cinvestav: Departamento de Computacion, Evolutionary Computation GroupCarlos, C.A.C., Constraint-handling using an evolutionary multiobjective optimization technique (2000) Civil Engineering and Environmental Systems, 17 (4), pp. 319-346;
Cai, Z.X., Wang, Y., A multi-objective optimization-based evolutionary algorithm for constrained optimization (2006) IEEE Trans. on Evolutionary Computation, 10 (6), pp. 658-675;
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Kennedy, J., Eberhert, R., Particle swarm optimization (1995) Proc. of the IEEE International Conference on Neural Networks, pp. 1942-1948;
Karaboga, D., An idea based on honey bee swarm for numerical optimization (2005), Kayseri: Erciyes UniversityBasturk, B., Karaboga, D., An artificial bee colony (ABC) algorithm for numeric function ptimization (2006) Proc. of the IEEE Swarm Intelligence Symposium, pp. 3-4;
Karaboga, D., Basturk, B., Ozturk, C., Artificial bee colony (ABC) optimization algorithm for solving constrained optimization (2007) Proc. of the Foundations of Fuzzy Logic and Soft Computing, pp. 789-798
DOCUMENT TYPE: Article
SOURCE: Scopus
Liu, J., Ren, X., Ma, H.
A new PSO algorithm with Random C/D Switchings
(2012) Applied Mathematics and Computation, 218 (19), pp. 9579-9593.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860443283&partnerID=40&md5=b1833f26f685f64e332443a91d2883a0
AFFILIATIONS: School of Automation, Beijing Institute of Technology, 100081 Beijing, China
ABSTRACT: This paper investigates the overall convergence analysis and proposes a novel Random C/D Switchings PSO algorithm with random switchings between convergence operator and divergence operator. With respect to the standard PSO algorithm, its convergence analysis provides a fundamental theory of selecting convergence operator and divergence operator. During the process of finding the suboptimal or global solution, the random switchings between two typical operators, namely Operator C and Operator D, which are two different ways to update velocities of all particles, are conducted by a so-called convergence ratio parameter, which can determine the tradeoff between exploration ability and exploitation ability from the quantitative perspective. Numerical results on several benchmark functions demonstrate the following observations: (1) The proper convergence ratio is closely related to the landscape of objective function, the dimension of solution space and the number of local optimums. (2) Small convergence ratio, setting to 0.60 or 0.65, may benefit the optimization problem which has many local optimums in the high dimensional space; while large convergence ratio, setting to 0.85 or 0.9, is probably helpful for the optimization problem with few local optimums or flat landscape. © 2012 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Constriction factor; Convergence analysis; Inertia weight; Particle swarm optimization; Random switching
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Eberhart, R.C., Shi, Y., Comparison between genetic algorithms and particle swarm optimization (1998) Proceedings of the 7th International Conference Evolutionary Programming VII, pp. 611-616;
Clerc, M., Kennedy, J., The particle swarm-explosion, stability, and convergence in a multidimensional complex space (2002) IEEE Transactions on Evolutionary Computation, 6 (1), pp. 58-73., DOI 10.1109/4235.985692, PII S1089778X02022099;
Carlisle, A., Dozier, G., An off-the-shelf PSO (2001) Proceedings of the 2001 Workshop on Particle Swarm Optimization, pp. 1-6;
Parsopoulos, K.E., Tasoulis, D.K., Vrahatis, M.N., Multiobjective optimization using parallel vector evaluated particle swarm optimization (2004) Proceedings of the IASTED International Conference. Applied Informatics, pp. 823-828., 411-177, Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics);
Trelea, I.C., The particle swarm optimization algorithm: Convergence analysis and parameter selection (2003) Information Processing Letters, 85 (6), pp. 317-325;
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DOCUMENT TYPE: Review
SOURCE: Scopus
Gao, X.-Z.a , Wang, X.a , Jokinen, T.b , Ovaska, S.J.b , Arkkio, A.b , Zenger, K.a
A hybrid optimization method for wind generator design
(2012) International Journal of Innovative Computing, Information and Control, 8 (6), pp. 4347-4373.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861414861&partnerID=40&md5=cc6852a2756274b797b0bd070273dd19
AFFILIATIONS: Department of Automation and Systems Technology, School of Electrical Engineering, Aalto University, P. O. Box 15500, FI-00076 Aalto, Finland;
Department of Electrical Engineering, School of Electrical Engineering, Aalto University, P. O. Box 13000, FI-00076 Aalto, Finland
ABSTRACT: The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, the HS does not store or utilize the useful knowledge gained during the search procedure in an efficient way. In this paper, we propose and study a hybrid optimization approach, in which the HS is merged together with the Cultural Algorithm (CA). Our modified HS method, namely HS-CA, has the interesting feature of embedded problem-solving knowledge. Simulations of some typical benchmark problems demonstrate that the HS-CA can yield a superior optimization performance over the regular HS algorithm. We also apply the proposed HS-CA in a case study of the optimal wind generator design to further examine its effectiveness. © 2012 ICIC International.
AUTHOR KEYWORDS: Cultural algorithm (CA); Harmony search (HS); Hybrid optimization methods; Search knowledge; Wind generator optimization
REFERENCES: Geem, Z.W., Kim, J.H., Loganathan, G.V., A new heuristic optimization algorithm: Harmony search (2001) Simulation, 76 (2), pp. 60-68;
Lee, K.S., Geem, Z.W., A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice (2005) Computer Methods In Applied Mechanics and Engineering, 194 (36-38), pp. 3902-3922;
Lee, K.S., Geem, Z.W., A new structural optimization method based on the harmony search algorithm (2004) Computers and Structures, 82 (9-10), pp. 781-798;
Geem, Z.W., Kim, J.H., Loganathan, G.V., Harmony search optimization: Application to pipe network design (2002) International Journal of Modeling and Simulation, 22 (2), pp. 125-133;
Alia, O.M., Mandava, R., Aziz, M.E., A hybrid harmony search algorithm for MRI brain segmentation (2011) Evolutionary Intelligence, 4 (1), pp. 31-49;
Nahas, N., Thien-My, D., Harmony search algorithm: Application to the redundancy optimization problem (2010) Engineering Optimization, 42 (9), pp. 845-861;
Geem, Z.W., Novel derivative of harmony search algorithm for discrete design variables (2008) Applied Mathematics and Computation, (1), pp. 223-230;
Omran, M.G.H., Mahdavi, M., Global-best harmony search (2008) Applied Mathematics and Computation, 198 (2), pp. 643-656;
Gao, X.-Z., Wang, X., Ovaska, S.J., Uni-modal and multi-modal optimization using modified harmony search method (2009) International Journal of Innovative Computing, Information and Contro, 5 (10), pp. 2985-2996;
Gao, X.-Z., Wang, X., Ovaska, S.J., Xu, H., A modified harmony search method in constrained optimization (2010) International Journal of Innovative Computing, Information and Control, 6 (9), pp. 4235-4247;
Wang, X., Gao, X.Z., Ovaska, S.J., Fusion of clonal selection algorithm and harmony search method in optimization of fuzzy classification systems (2009) International Journal of Bio-Inspired Computation, 1 (1-2), pp. 80-88;
Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., Panigrahi, B.K., Exploratory power of the harmony search algorithm: Analysis and improvements for global numerical optimization (2011) IEEE Trans, On Systems, Man, and Cybernetics, Part B: Cybernetics, 41 (1), pp. 89-106;
Reynolds, R.G., Peng, B., Cultural algorithms: Modeling how cultures learn to solve problems (2004) Proc. of the IEEE International Conference On Tools With Artificial Intelligence, pp. 166-173., Boca Raton, FL;
Reynolds, R.G., Chung, C.J., CAEP: An evolution-based tool for real-valued function optimization using cultural algorithms (1998) International Journal On Artificial Intelligence Tools, 7 (3), pp. 239-293;
Poli, R., Langdon, W.B., (2002) Foundations of Genetic Programming, , Springer, Berlin, Germany;
Krug, M., Nguang, S.K., Wu, J., Shen, J., GA-based model predictive control of boiler-turbine systems (2010) International Journal of Innovative Computing, Information and Control, 6 (11), pp. 5237-5248;
Engelbrecht, A.P., (2005) Fundamentals of Computational Swarm Intelligence, , John Wiley & Sons Ltd., West Sussex, England;
Lin, C.-J., Wang, J.-G., Chen, S.-M., 2D/3D face recognition using neural network based on hybrid Taguchi-particle swarm optimization (2011) International Journal of Innovative Computing, Information and Control, 7 (2), pp. 537-553;
Cai, X., Cui, Z., Zeng, J., Tan, Y., Particle swarm optimization with self-adjusting cognitive selection strategy (2008) International Journal of Innovative Computing, Information and Control, 4 (4), pp. 943-952;
Storn, R., Price, K., Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces (1997) Journal of Global Optimization, 11, pp. 341-359;
Vegh, V., Pierens, G.K., Tieng, Q.M., A variant of differential evolution for discrete optimization problems requiring mutually distinct variables (2011) International Journal of Innovative Computing, Information and Control, 7 (2), pp. 897-914;
Reynolds, R.G., Chung, C.J., Knowledge-based self-adaptation in evolutionary programming using cultural algorithms (1997) Proc. of the IEEE International Conference On Evolutionary Computation, pp. 71-76., Indianapolis, IN;
Goldberg, D.E., (1989) Genetic Algorithms In Search, Optimization, and Machine Learning, , Addison- Wesley, Reading, MA;
Kramer, O., Evolutionary self-adaptation: A survey of operators and strategy parameters (2010) Evolutionary Intelligence, 3 (2), pp. 51-65;
Michalewicz, Z., (1996) Genetic Algorithms + Data Structures = Evolution Programs, , 3rd Edition, Springer, Berlin, Germany;
Benchmark Functions, , http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm;
Mahdavi, M., Fesanghary, M., Damangir, E., An improved harmony search algorithm for solving optimization problems (2007) Applied Mathematics and Computation, 188 (2), pp. 1567-1579;
Deb, K., Optimal design of a welded beam via genetic algorithms (1991) Journal of American Institute of Aeronautics and Astronautics, 29 (11), pp. 2013-2015;
Parsopoulos, K.E., Vrahatis, M.N., Unified particle swarm optimization for solving constrained engineering optimization problems (2005) Lecture Notes In Computer Science, 3612, pp. 582-591;
Coello, C.A.C., Constraint-handling using an evolutionary multiobjective optimization technique (2000) Civil Engineering and Environmental Systems, 17 (4), pp. 319-346;
Pyrhönen, J., Jokinen, T., Hrabovcová, V., (2008) Design of Rotating Electrical Machines, , John Wiley & Sons Ltd., West Sussex, UK;
Gao, X.Z., Jokinen, T., Wang, X., Ovaska, S.J., Arkkio, A., A new harmony search method in optimal wind generator design (2010) Proc. of the XIX International Conference On Electrical Machines, , Rome, Italy;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Mategaonkar, M., Eldho, T.I.
Groundwater remediation optimization using a point collocation method and particle swarm optimization
(2012) Environmental Modelling and Software, 32, pp. 37-48.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857373751&partnerID=40&md5=b516468495170795c4d4cc13a8a1d489
AFFILIATIONS: Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
ABSTRACT: Groundwater contamination is a major problem in many parts of the world. Remediation of contaminated groundwater is a tedious, time consuming and expensive process. Pump and treat (PAT) is one of the commonly used techniques for groundwater remediation. Simulation-optimization (S/O) models are very useful in appropriate design of an effective PAT remediation system. Simulation models can be employed to predict the spatial and temporal variation of contaminant plumes. Optimization models, on the other hand, can be used to minimize the cost of pumping or recharge. Generally, grid or mesh based models using Finite Difference Methods (FDM) or Finite Element Methods (FEM) are used for groundwater flow and transport simulation. Recently, Meshfree (MFree) based numerical models have been developed due to the difficulty of meshing and remeshing in these methods. The MFree Point Collocation Method (PCM) is a simple MFree method to simulate coupled groundwater flow and contaminant transport. It saves time for pre-processing such as meshing or remeshing. Evolutionary algorithm based techniques such as for particle swarm optimization (PSO) and genetic algorithms (GA) have been found to be very effective for groundwater optimization problems. In this paper, a simulation model using MFree PCM for unconfined groundwater flow and transport and a PSO based optimization model are developed. These models are coupled to get an effective S/O model for the groundwater remediation design using PAT. The S/O model is applied to the remediation design of an unconfined field aquifer polluted by Total Dissolved Solids (TDS) by using pump and treat and flushing. The model provides an effective remediation design of pumping rate for the selected wells and costs of remediation. © 2012 Elsevier Ltd.
AUTHOR KEYWORDS: Groundwater remediation; Particle swarm optimization; Point collocation method; Pump and treat; Simulation-optimization
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DOCUMENT TYPE: Article
SOURCE: Scopus
Liu, H.a b c , Abraham, A.c d , Snášel, V.c d , McLoone, S.e
Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments
(2012) Information Sciences, 192, pp. 228-243. Cited 1 time.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862777930&partnerID=40&md5=e625b136c237788587da6be9b09c0def
AFFILIATIONS: School of Information, Dalian Maritime University, 116026 Dalian, China;
School of Computer, Dalian University of Technology, 116023 Dalian, China;
Machine Intelligence Research Labs, Auburn, WA 98071, United States;
Department of Computer Science, VŠB-Technical University of Ostrava, 708 33 Ostrava-Poruba, Czech Republic;
Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland
ABSTRACT: The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Distributed data-intensive computing environments; Particle swarm; Scheduling problem; Security constraints; Swarm intelligence; Work-flow
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DOCUMENT TYPE: Article
SOURCE: Scopus
Cui, Z.a b , Cai, X.a , Zeng, J.a
A new stochastic algorithm to direct orbits of chaotic systems
(2012) International Journal of Computer Applications in Technology, 43 (4), pp. 366-371. Cited 1 time.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860763039&partnerID=40&md5=47317e145b700ee432011e3fed5fed09
AFFILIATIONS: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, 030024, China;
State Key Laboratory of Novel Software Technology, Nanjing University, 210093, China
ABSTRACT: In this paper, a new stochastic optimisation algorithm is introduced to simulate the plant growing process. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomena. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction. In our algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Phototropism operator is designed to introduce the fitness function value, as well as to decide the growing direction. To test the performance, it is used to solve the directing orbits of chaotic systems, simulation results show this new algorithm increases the performance significantly when compared with other four optimisation algorithms. Copyright © 2012 Inderscience Enterprises Ltd.
AUTHOR KEYWORDS: Directing orbits of chaotic systems; Photosynthesis operator; Phototropism operator
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DOCUMENT TYPE: Article
SOURCE: Scopus
Peng, Z.a b , Wu, L.c
Gradient descent based multi-relationship fuzzy cognitive map mining
(2012) Journal of Convergence Information Technology, 7 (10), pp. 345-351.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863108425&partnerID=40&md5=97008f99b9d5937134718ed5cb2c90e1
AFFILIATIONS: Department of Computer, North China Institute of Science and Technology, China;
School of Computer, Qinghai Normal University, China;
Information Engineering College, Capital Normal University, China
ABSTRACT: In order to better mine knowledge from complex multi-relationship system, gradient descent based multi-relationship FCM(Fuzzy Cognitive Map) mining method is proposed. Based on a kind of multi-relationship FCM, TTFCM(Two-level Tree-type Fuzzy Cognitive Map), gradient descent based TTFCM weight learning(GD_TTFCM), and classification inference(INF_TTFCM) are implemented and applied in multi-relationship. Finally, the experiments in financial multi-relationship dataset demonstrate the efficiency of the methods.
AUTHOR KEYWORDS: FCM(Fuzzy Cognitive Map); GD-TTFCM, INF-TTFCM; TTFCM(Two-level Tree-type Fuzzy Cognitive Map)
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Stach, W., Kurgan, L., Pedrycz, W., A divide and conquer method for learning large Fuzzy Cognitive Maps (2010) Fuzzy Sets and Systems, 161 (1), pp. 2515-2532;
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Xu, G., Yang, B., Qin, Y., New multi-relational naive Bayesian classifier (2008) Systems Engineering and Electronics, 30 (4), pp. 655-657;
Xu, G., Yang, B., Qin, Y., Zhang, W., Multi-relational Naive Bayesian classifier based on mutual information (2008) Journal of University of Science and Technology Beijing, 30 (8), pp. 963-966
DOCUMENT TYPE: Article
SOURCE: Scopus
Oh, S.a , Ahn, C.W.b , Jeon, M.a
Effective constraints based evolutionary algorithm for constrained optimization problems
(2012) International Journal of Innovative Computing, Information and Control, 8 (6), pp. 3997-4014.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861414872&partnerID=40&md5=542acfea904ba98b644572534a08b3d6
AFFILIATIONS: School of Information and Communications, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro (Oryong-dong), Buk-gu, Gwangju 500-712, South Korea;
School of Information and Communication Engineering, Sungkyunkwan University, 300 Cheonchoen-dong, Jangan-gu, Suwon, Gyeonggi-do 440-746, South Korea
ABSTRACT: Evolutionary algorithms (EAs) have an enviable success record in solving many constrained optimization problems (COPs) in a variety of areas. However, those problems are afflicted by the highly constrained feasibility - isolated and small feasible regions, since EAs should thoroughly test feasibility for all constraints at every generation. To systematically deal with its limitation, this paper presents a new approach for effectively handling the constraints of COPs. The major idea is to extract an actual subset of meaningful constraints, termed effective constraints, from the current population. This discovered set plays a key role in satisfying the feasibility within the certain tolerance specified by the statistics on feasible solutions and several prefixed criteria on feasibility. Thanks to the proposed effective constraints, it is able to evolve the population toward the legitimate region of the search space without assessing all the constraints; thus, the better feasible space is yielded than that of originals. The proposed constraint handling technique is combined with a widely-used EA, stochastic ranking evolutionary strategy, for achieving the optimal solutions of COPs. The proposed algorithm is compared with several well-known references on three real-world engineering optimization problems. Computational studies verify that the proposed algorithm achieves better solutions than those of the existing algorithms. © 2012 ICIC International.
AUTHOR KEYWORDS: Effective constraints; Engineering optimization problems; Feasibility statistics; Stochastic ranking evolutionary strategy
REFERENCES: Guo, Y., Cao, X., Zhang, J., Constraint handling based multiobjective evolutionary algorithm for aircraft landing scheduling (2009) International Journal of Innovative Computing, Information and Control, 5 (8), pp. 2229-2238;
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Liu, C., An evolutionary algorithm for solving dynamic nonlinear constrained optimization (2010) ICIC Express Letters, 4 (3 B), pp. 1039-1044;
Oh, S., Jin, Y., Jeon, M., Approximate models for constraint functions in evolutionary constrained optimization (2011) International Journal of Innovative Computing, Information and Control, 7 (11-12), pp. 6585-6603;
Coello, C.C.A., Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art (2002) Computer Methods In Applied Mechanics and Engineering, 191 (11-12), pp. 1245-1287;
Coello, C.C.A., Use of a self-adaptive penalty approach for engineering optimization problems (2000) Computers In Industry, 41 (2), pp. 113-127;
Hamida, S.B., Schoenauer, M., ASCHEA: New results using adaptive segregational constraint handling (2002) Proc. of IEEE Conference On Evolutionary Computation 2002, pp. 82-87., Honolulu, HI, USA;
Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method for constrained optimization problems (2002) Proc. of the Euro-International Symposium On Computational Intelligence 2002, pp. 214-220., Kosice, Slovakia;
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Coello, C.C.A., Montes, E.M., Constraint-handling in genetic algorithms through the use of dominance-based tournament selection (2002) Advanced Engineering Informatics, 16 (3), pp. 193-203;
Montes, E.M., Coello, C.C.A., A simple multimembered evolution strategy to solve constrained optimization problems (2005) IEEE Transactions On Evolutionary Computation, 9 (1), pp. 1-17;
Cagnina, L.C., Esquivel, S.C., Coello, C.C.A., Solving engineering optimization problems with the simple constrained particle swarm optimizer (2008) Informatica, 32, pp. 319-326;
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Chootinan, P., Chen, A., Constraint handling in genetic algorithms using a gradient-based repair method (2006) Computer and Operations Research, 33, pp. 2263-2281;
Zahara, E., Kao, Y.-T., Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems (2009) Expert Systems With Applications, 36 (2), pp. 3880-3886;
Belur, S.V., (1997) CORE: Constrained Optimization By Random Evolution, , Late Breaking Papers at the Genetic Programming 1997 Conference, CA, USA;
Forrest, S., Perelson, A.S., Genetic algorithms and the immune system (1991) Proc. of the 1st Workshop On Parallel Problem Solving From Nature, pp. 320-325., Berlin, Germany;
Reynolds, R.G., An introduction to cultural algorithms (1994) Proc. of the 3rd Annual Conference On Evolutionary Programming, pp. 131-134;
Zhang, X., Lu, Q., Wen, S., Wu, M., Wang, X., A modified differential evolution for constrained optimization (2008) ICIC Express Letters, 2 (2), pp. 181-186;
Wang, Y., Cai, Z., Zhou, Y., Zeng, W., An adaptive tradeoff model for constrained evolutionary optimization (2008) IEEE Transactions On Evolutionary Computation, 12 (1), pp. 80-92;
Ragsdell, K., Phillips, D., Optimaldesign of a class of welded structures using geometric programming (1976) Journal of Engineering For Industry, 98 (3), pp. 1021-1025;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Sujit, P.B.a , Lucani, D.E.b , Sousa, J.B.a
Bridging cooperative sensing and route planning of autonomous vehicles
(2012) IEEE Journal on Selected Areas in Communications, 30 (5), art. no. 6214702, pp. 912-922.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862270305&partnerID=40&md5=29b71a7fbceef5f46d6a3b562935640f
AFFILIATIONS: Department of Electrical and Computer Engineering, Faculdade de Engenharia, Universidade do Porto, Portugal;
Instituto de Telecomunicações, DEEC, Universidade do Porto, Portugal
ABSTRACT: Autonomous Vehicles (AV) are used to solve the problem of data gathering in large scale sensor deployments with disconnected clusters of sensors networks. Our take is that an efficient strategy for data collection with AVs should leverage i) cooperation amongst sensors in communication range of each other forming a sensor cluster, ii) advanced coding and data storage techniques for easing the cooperation process, and iii) AV route-planning that is both content- and cooperation-aware. Our work formulates the problem of efficient data gathering as a cooperative route-optimization problem with communication constraints. We also analyze (network) coded data transmission and storage for simplifying cooperation amongst sensors as well as data collection by the AV. Given the complexity of the problem, we focus on heuristic techniques, such as particle swarm optimization, to calculate the AV's route and the times for communication with each sensor and/or cluster of sensors. We analyze two extreme cases, i.e., networks with and without intra-cluster cooperation, and provide numerical results to illustrate that the performance gap between them increases with the number of nodes. We show that cooperation in a 100 sensor deployment can increase the amount of data collected by up to a factor of 3 with respect to path planning without cooperation. © 2012 IEEE.
AUTHOR KEYWORDS: autonomous vehicles; network coding; optimization; path planning; Robot sensing systems
REFERENCES: Ahlswede, R., Cai, N., Li, S.Y., Yeung, R., Network information flow (2000) IEEE Trans. Inf. Theory, 46 (4), p. 12041216., Jul;
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Leong, D., Dimakis, A.G., Ho, T., Symmetric allocations for distributed storage (2010) Proc. IEEE Global Telecomm. Conf. (GLOBECOM), pp. 1-6., Hawaii, USA, Dec;
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Enright, J.J., Savla, K., Frazzoli, E., Bullo, F., Stochastic and dynamic routing problems for multiple uavs (2009) AIAA Journal of Guidance, Control and Dynamics, 32 (4), pp. 1152-1166;
Rathinam, S., Sengupta, R., Lower and upper bounds for a multiple depot uav routing problem (2006) Proc. 45th IEEE Conf. on Decision and Control, pp. 5287-5292., San Diego, USA, Dec;
Lindh́e, M., Johansson K, H., Communication-aware trajectory tracking (2008) IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 1519-1524., Pasadena, USA, May;
Tekdas, O., Karnad, N., Isler, V., Efficient strategies for collecting data from wireless sensor network nodes using mobile robots (2009) Proc. 14th Inter. Symp. on Robotics Research (ISRR), , Lucerne, Switzerland;
Ghaffarkhah, A., Mostofi, Y., Communication-aware navigation functions for cooperative target tracking (2009) Proc. American Control Conf. (ACC), pp. 1316-1322., Missouri, USA, Jun;
Le, V.T., Bouraqadi, N., Stinckwich, S., Moraru, V., Doniec, A., Making networked robots connectivity-aware (2009) Proc. IEEE Int. Conf. on Rob. and Auto. (ICRA'09), pp. 1835-1840., Kobe, Japan, May;
Ueda, T., Tanaka, S., Komiyama, B., Roy, S., Saha, D., Bandyopadhyay, S., Acr: An adaptive communication-aware routing through maximally zone-disjoint shortest paths in ad hoc wireless networks with directional antenna (2006) Wireless Comm. and Mobile Comp., 6 (2), p. 191199., Mar;
Larsen, A., Madsen, O.B.G., Solomon, M.M., Classification of dynamic vehicle routing systems (2007) Dynamic Fleet Management (, pp. 19-40., Springer US;
Li, S.Y.R., Yeung, R.W., Cai, N., Linear network coding (2003) IEEE Trans. Inf. Theory, 49, p. 371., Feb;
Koetter, R., Ḿedard, M., An algebraic approach to network coding (2003) IEEE/ACM Trans. Netw., 11 (5), pp. 782-795., Oct;
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Deb, S., Ḿedard, M., Choute, C., Algebraic gossip: A network coding approach to optimal multiple rumor mongering (2006) IEEE Trans. on Info. Theory, 52 (6), pp. 2486-2507., Jun;
Haeupler, B., Analyzing network coding gossip made easy Proc. 43rd annual ACM Symp. on Theory of Comp (STOC, 2011, pp. 293-302., San Jose, CA, USA, Jun;
Parsopoulos, K., Vrahatis, M., Particle swarm optimization method in multiobjective problems (2002) Proc. of the ACM Symp. on Applied Comp. (SAC), pp. 603-607., Madrid, Spain, Mar;
Coello C, C., Salazar Lechuga, M., Mopso: A proposal for multiple objective particle swarm optimization (2002) Proc. IEEE Cong. on Evol. Comp. (CEC)., pp. 1051-1056., Hawaii, US, May;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Zhang, T., Conklin, G., Zhang, Y., Dougal, R.A.
Accounting for "mission" during co-optimization of system designs
(2012) SysCon 2012 - 2012 IEEE International Systems Conference, Proceedings, art. no. 6189520, pp. 334-341.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861310700&partnerID=40&md5=695906f4b7f7e2ab3270d3c1d06a3f14
AFFILIATIONS: Dept. of Electrical Engineering, University of South Carolina, Columbia, SC, United States
ABSTRACT: A simulation-based approach to optimal design of systems requires collective solution to three related problems: selection of the best equipment, selection of the best system configuration, and optimal operation of the system in a mission-oriented sense. We solve these three problems by applying an improved Particle Swarm Optimization (PSO) algorithm on top of a toolbox that rapidly instantiates system models from high level specifications and system templates. The improved PSO more efficiently handles the involvement of discrete binary variables in the objective functions with universal constraints, more effectively avoids premature convergence, and yields a more accurate search for the global optimum solution. Our simulation-component-based co-optimization approach to system designs is illustrated by applying it to the design of an electric ship power system while accounting for the unit commitment problem during a set of missions. First, we prove the efficacy of the new PSO algorithm on one candidate system, and then we apply the new PSO method to evaluate and compare three candidate designs. The improved PSO shows that the system should actually consume up to 52% less fuel than is predicted by a simpler evaluation that invokes a proportionally-distributed power alignment; this is a much better measure of the true system performance. As compared with another validated hybrid PSO-GA algorithm, the improved PSO consistently finds an optimum operating point that yields up to 2.4% better fuel efficiency, and it improves the reliability of solutions by up to 32%. Next, three candidate designs are evaluated under optimal operating conditions, for a given mission profile, and compared in terms of their best performance. The simulation results successfully determine the best genset combination from the competing designs. © 2012 IEEE.
AUTHOR KEYWORDS: electric ship; mission-oriented; Particle Swarm Optimization; system co-optimization; unit commitment
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Zhang, Y., Lum, K., Integrated-optimal design of airplane and flight control using genetic algorithms (2007) IEEE CEC 2007, pp. 2980-2987., Singapore, Sept;
Hanini, N., Tabbache, B., Kheloui, A., Roubache, T., Sizing methodology of EV drive system based on optimal power efficiency (2008) 2008 International SPEEDAM, Ischia, pp. 1043-1048., Jun;
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Verdaasdonk, J., Grimmelius, H., Bil, C., Anavatti, S., Comprehensive design tool for sizing and simulation of autonomous underwater vehicles (2007) OCEANS 2007 - Europe, pp. 1-5., June;
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Venkatesh, P., Gnanadass, R., Padhy, N., Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints (2003) IEEE Trans. on Power Systems, 18 (2), pp. 688-697., May;
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Kennedy, J., Eberhart, R., Particle swarm optimization (1995) IEEE Proc. the International Conference on Neural Networks, Perth, Australia, pp. 1942-1948;
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Virtual Test Bed Overview, , http://vtb.engr.sc.edu/vtbwebsite/#/Overview;
Conklin, G., Dougal, R., Langland, B., Using Simulation Generation Templates to Interface Simulation Analysis Tools with Design Space Models, , unpublished;
Wang, L., Singh, C., Unit commitment considering generator outages through a mixed-integer particle swarm optimization algorithm (2009) Applied Soft Computing, 9, pp. 947-953;
Joines, J., Houck, C., On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's (1994) IEEE Int. Conf. Evol. Comp, pp. 579-585., Jun;
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Toutouh, J., García-Nieto, J., Alba, E.
Intelligent OLSR routing protocol optimization for VANETs
(2012) IEEE Transactions on Vehicular Technology, 61 (4), art. no. 6166905, pp. 1884-1894. Cited 1 time.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861137821&partnerID=40&md5=76b9d69cb18dc737763b1d05462034cf
AFFILIATIONS: Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, Ḿlaga, Spain
ABSTRACT: Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing packets through the network is a challenging task. Therefore, offering an efficient routing strategy is crucial to the deployment of VANETs. This paper deals with the optimal parameter setting of the optimized link state routing (OLSR), which is a well-known mobile ad hoc network routing protocol, by defining an optimization problem. This way, a series of representative metaheuristic algorithms (particle swarm optimization, differential evolution, genetic algorithm, and simulated annealing) are studied in this paper to find automatically optimal configurations of this routing protocol. In addition, a set of realistic VANET scenarios (based in the city of Mlaga) have been defined to accurately evaluate the performance of the network under our automatic OLSR. In the experiments, our tuned OLSR configurations result in better quality of service (QoS) than the standard request for comments (RFC 3626), as well as several human experts, making it amenable for utilization in VANET configurations. © 2012 IEEE.
AUTHOR KEYWORDS: Metaheuristics; optimization algorithms; optimized link state routing (OLSR); vehicular ad hoc networks (VANET)
REFERENCES: Hartenstein, H., Laberteaux, K., (2009) VANET Vehicular Applications and Inter-Networking Technologies, , Upper Saddle River NJ: Wiley, Dec. ser. Intelligent Transport Systems;
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Lu, N., Ji, Y., Liu, F., Wang, X., A dedicated multi-channel MAC protocol design for VANET with adaptive broadcasting (2010) Proc. IEEE WCNC, pp. 1-6., Apr;
Taleb, T., Sakhaee, E., Jamalipour, A., Hashimoto, K., Kato, N., Nemoto, Y., A stable routing protocol to support ITS services in VANET networks (2007) IEEE Transactions on Vehicular Technology, 56 (6), pp. 3337-3347., DOI 10.1109/TVT.2007.906873;
Li, F., Wang, Y., Routing in vehicular ad hoc networks: A survey (2007) IEEE Vehicular Technology Magazine, 2 (2), pp. 12-22., DOI 10.1109/MVT.2007.912927;
Zhang, W., Festag, A., Baldessari, R., Le, L., Congestion control for safety messages in VANETs: Concepts and framework (2008) Proc. 8th ITST, pp. 199-203., Oct;
Clausen, T., Jacquet, P., Optimized link state routing protocol (OLSR) (2003) IETF RFC 3626, , http://www.ietf.org/rfc/rfc3626.txt;
Chen, T., Mehani, O., Boreli, R., Trusted routing for VANET (2009) Proc. 9th Int. Conf. ITST, pp. 647-652., M. Berbineau,M. Itami, and G.Wen, Eds Oct;
Haerri, J., Filali, F., Bonnet, C., Performance comparison of AODV and OLSR in VANETs urban environments under realistic mobility patterns (2006) Proc. 5th Annu. Med-Hoc-Net, , S. Basagni, A. Capone, L. Fratta, and G. Morabito, Eds., Lipari, Italy Jun;
Laouiti, A., Mühlethaler, P., Sayah, F., Toor, Y., Quantitative evaluation of the cost of routing protocol OLSR in a Vehicle ad hoc NETwork (VANET) (2008) Proc. VTC, pp. 2986-2990;
Santa, J., Tsukada, M., Ernst, T., Mehani, O., Gómez-Skarmeta, A.F., Assessment of VANET multi-hop routing over an experimental platform (2009) Int. J. Internet Protocol Technol., 4 (3), pp. 158-172., Sep;
Gomez, C., Garcia, D., Paradells, J., Improving performance of a real ad-hoc network by tuning OLSR parameters (2005) Proceedings - IEEE Symposium on Computers and Communications, pp. 16-21., Proceedings - 10th IEEE Symposium on Computers and Communications, ISCC 2005;
Blum, C., Roli, A., Metaheuristics in combinatorial optimization: Overview and conceptual comparison (2003) ACM Comput. Surv., 35 (3), pp. 268-308., Sep;
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Alba, E., García-Nieto, J., Taheri, J., Zomaya, A., New research in nature inspired algorithms for mobility management in GSM networks (2008) Proc. EvoWorkshops-LNCS, pp. 1-10;
Alba, E., Dorronsoro, B., Luna, F., Nebro, A.J., Bouvry, P., Hogie, L., A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs (2007) Computer Communications, 30 (4), pp. 685-697., DOI 10.1016/j.comcom.2006.08.033, PII S0140366406003276, Nature-Inspired Distributed Computing;
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Ge, Y., Kunz, T., Lamont, L., Quality of service routing in ad-hoc networks using OLSR (2003) Proc. 36th HICSS, pp. 9-18;
Huhtonen, A., Comparing AODV and OLSR routing protocols (2004) Proc. Telecommun. Softw. Multimedia, pp. 1-9;
Huang, Y., Bhatti, S., Parker, D., Tuning OLSR (2006) Proc. IEEE 17th Int. Symp. PIMRC, pp. 1-5., Helsiniki, Finland;
Nguyen, D., Minet, P., Analysis of MPR selection in the OLSR protocol (2007) Proceedings - 21st International Conference on Advanced Information Networking and Applications Workshops/Symposia, AINAW'07, 1, pp. 887-892., DOI 10.1109/AINAW.2007.94, 4224218, Proceedings - 21st International Conference on Advanced Information Networking and ApplicationsWorkshops/ Symposia, AINAW'07;
Alba, E., Luna, S., Toutouh, J., Accuracy and efficiency in simulating VANETs (2008) Proc. MCO-Communications Computer Information Science, 14, pp. 568-578., L. T. H. An, P. Bouvry, and T. P. Dinh, Eds;
Krajzewicz, D., Bonert, M., Wagner, P., The open source traffic simulation package SUMO (2006) Proc. RoboCup, pp. 1-10., Bremen, Germany;
Alba, E., Luque, G., García-Nieto, J., Ordonez, G., Leguizamón, G., MALLBA: A software library to design efficient optimisation algorithms (2007) Int. J. Innov. Comput. Appl., 1 (1), pp. 74-85., Apr;
Sheskin, D.J., (2007) Handbook of Parametric and Nonparametric Statistical Procedures, , London U.K.: Chapman & Hall;
Eberhart, R., Shi, Y., Comparing inertia weights and constriction factors in particle swarm optimization (2000) Proc. IEEE CEC, 1, pp. 84-88., La Jolla, CA;
Toutouh, J., Alba, E., An efficient routing protocol for green communications in vehicular ad-hoc networks (2011) Proc. 13th Annu. Conf. Companion GECCO, pp. 719-726;
Taliwal, V., Jiang, D., Mangold, H., Chen, C., Sengupta, R., Empirical determination of channel characteristics for DSRC vehicle-to-vehicle communication (2004) VANET - Proceedings of the First ACM International Workshop on Vehicular Ad Hoc Networks, p. 88., VANET - Proceedings of the First ACM International Workshop on Vehicular Ad Hoc Networks
DOCUMENT TYPE: Article
SOURCE: Scopus
Pierre, D.M., Zakaria, N., Pal, A.J.
Quantitative and qualitative analysis of unmanned aerial vehicle's path planning using master-slave parallel vector-evaluated genetic algorithm
(2012) Advances in Intelligent and Soft Computing, 130 AISC (VOL. 1), pp. 567-577.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861150369&partnerID=40&md5=fe43eaac7626a015a2dc2a9afdf52536
AFFILIATIONS: High-Performance Computing Center, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
ABSTRACT: The demand of Unmanned Aerial Vehicle (UAV) to monitor natural disasters extends its use to multiple civil missions. While the use of remotely control UAV reduces the human casualties' rates in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. In order to automate UAVs, several approaches to path planning have been proposed. However, none of the proposed paradigms optimally solve the path planning problem with contrasting objectives. We are proposing a Master-Slave Parallel Vector-Evaluated Genetic Algorithm (MSPVEGA) to solve the path planning problem. MSPVEGA takes advantage of the advanced computational capabilities to process multiple GAs concurrently. In our present experimental set-up, the MSPVEGA gives optimal results for UAV. © 2012 Springer India Pvt. Ltd.
AUTHOR KEYWORDS: Contrasting Objectives; Genetic Algorithm; Multi-objective; Path-planning; UAV
REFERENCES: Manning, S.D., Rash, C.E., Leduc, P.A., Noback, R.K., McKeon, J., The Role of Human Causal Factor in U.S. Army Unmanned Aerial Vehicle Accidents (2004) USAARL Report No. 2004-11, , March;
Fahimi, F., (2008) Autonomous Robots - Modeling, Path Planning, and Control;
Barraquand, J., Langlois, B., Latombe, J.-C., Numerical Potential Field Techniques for Robot Path Planning (1992) IEEE Transactions on Systems, Man, and Cybernetics, 22 (2)., March/April;
Kding, F.-G., Jiao, P., AUV Local Path Planning Based on Virtual Potential Field Proceedings of the IEEE International Conference on Mechatronics & Automation (July 2005);
Leigh, R., Louis, S.J., Miles, C., Using a Genetic Algorithm to Explore A*-like Pathfinding Algorithms (2007) Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Games (CIG 2007);
Graham, R., (2006) Real-time Agent Navigation with Neural Networks for Computer Games, , M.Sc Thesis;
Burchardt, H., Salomon, R., Implementation of Path Planning using Genetic Algorithms on Mobile Robots (2006) 2006 IEEE Congress on Evolutionary Computation, July 16-21;
Mittal, S., Deb, K., Three-Dimensional Offline Path Planning for UAVs Using Multiobjective Evolutionary Algorithms (2007) IEEE Congress on Evolutionary Computation (CEC 2007);
Parsopoulos, K.E., Tasoulis, D.K., Vrahatis, M.N., Multiobjective Optimization Using Parallel Vector Evaluated Particle Swarm Optimization;
Van Der Berg, J.P., Overmars, M.H., Roadmap-based motion planning in dynamic environments (2005) IEEE Transaction on Robotics, 21 (5), pp. 885-897;
Lingebach, F., (2005) Path Planning Using Probabilistic Cell Decomposition, , Licentiate Thesis Stockholm, Sweden;
Haddal, C.C., Gertler, J., (2010) Homeland Security: Unmanned Aerial Vehicles and Border Surveillance, , July 8;
Ismail, M.A., (2004) Parallel Genetic Algorithms (PGAs)-Master Slave Paradigm Approach Using MPI, , IEEE 0-7803-8655-8/04;
Beckhaus, S., Ritter, F., (2001) Cubicalpath - Dynamic Potential Fields for Guided Exploration in Virtual Environments
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Han, Q., Cao, W., Su, T.
Maintenance route planning based on particle swarm optimizattion algorithm
(2012) Proceedings - 2012 International Conference on Computer Science and Electronics Engineering, ICCSEE 2012, 2, art. no. 6187933, pp. 195-198.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861069182&partnerID=40&md5=588d00b69c380450a560956fc1f8ec1d
AFFILIATIONS: Naval Aeronautical and Astronautical University, Yantai, China
ABSTRACT: Maintaining route planning is a problem belonging to queuing theory. Firstly, PSO was introduced to the research of maintenance route planning. Secondly, constrains and the penalty functions were established based on the characters of the maintenances, and the procedure was presented. Finally, the numerical example was given out. The result shows the proposed algorithm yields faster convergence speed, higher accuracy solutions. © 2012 IEEE.
AUTHOR KEYWORDS: maintenance; PSO; queuing theory
REFERENCES: Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948., Piscataway, NJ, USA, IEEE Press;
Engelbrecht, A.P., (2005) Fundamentals of Computational Swarm Intelligence, , John Wiley & Sons, Chichester, West Sussex, England;
Poli, R., Kennedy, J., Blackwell, T., Particle swarm optimization. An overview (2007) Swarm Intelligence, 1 (1), pp. 33-57;
Sotiropoulos, D.G., Plagianakos, V.P., Vrahatis, M.N., An evolutionary algorithm for minimizing multimodal functions (2002) Proc. of the Fifth Hellenic-European Conf. on Comp. Math. and Its App., 2, pp. 496-500;
Schoeman, I.L., Engelbrecht, A.P., A parallel vector-based particle swarm optimizer (2005) Proc. of the International Conf. on Neural Networks and Genetic Algorithms, pp. 268-271;
Parsopoulos, K.E., Vrahatis, M.N., UPSO: A unified particle swarm optimization scheme (2004) Lecture Series on Comp. and Computational Sci., 1, pp. 868-873., Proc. of the Int. Conf. of Computational Methods in Sci. and Eng
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Ismail, M.M.
Improving the performance of the DTC saturated model of the induction motor in case of two level and three level VSI using GA and PSO algorithms
(2012) Proceedings of the 2012 Japan-Egypt Conference on Electronics, Communications and Computers, JEC-ECC 2012, art. no. 6186961, pp. 79-84.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860823896&partnerID=40&md5=deea4134e1f594261c69c93700a3f63e
AFFILIATIONS: Faculty of Engineering, Helwan University, Electrical Power and Machine Department, Egypt
ABSTRACT: The problem of controlling the -model induction motor with magnetic saturation is considered in this proposed research. Direct Torque Control (DTC) of induction motor has been developed since three decades. Furthermore many techniques have been proposed to improve the performance of the induction machines that using the DTC drives in industry. However, all the previous models are based on the linear model of the machine for approximation. This is not the exact model and there is no guarantee that the process will work outside the saturation region of the flux, especially in large rating of induction machines. In this paper, two types of voltage source inverters are applied on the saturated model of induction motor. We study the performance of the induction machine response in the two cases using MATLAB SIMULINK one is by using torque control and the other is by using speed control. GA and PSO are used for improvement of the speed control for the two and three level inverters. © 2012 IEEE.
AUTHOR KEYWORDS: Direct Torque Control (DTC); GA and PSO; Magnetically Saturated Induction Motors; Two and Three Level Inverter
REFERENCES: Bose, B.K., (2002) Modern Power Electronics and AC Drives, , Published by Prentice Hall PTR, Upper Saddle River, New Jersey;
Sorchini, Z., Krein, P.T., Formal derivation of direct torque control for induction machines (2006) IEEE Transactions on Power Electronics, 21 (5), pp. 1428-1436., DOI 10.1109/TPEL.2006.882086;
Nikolic, A., Jeftenic, B., Different methods for direct torque control of induction motor fed from current source inverter (2008) Wseas Transaction on Circuits and Systems, pp. 738-748., July;
Bose, B.K., (2002) Modern Power Electronics and AC Drives, , Published by Prentice Hall PTR, Upper Saddle River;
Wahab, H.A., Sanusi, H., Simulink model of direct torque control of induction machine (2008) American Journal of Applied Sciences, pp. 1083-1090;
Mohanty, K.B., A direct torque controlled induction motor with variable hysteresis band (2009) 11th International Conference on Computer Modeling and Simulation;
Somasekhar, V.T., Gopakumar, K., Three-level Inverter Configuration Cascading;
Ikonen, M., Laakkonen, O., Kettunen, M., Two-level and Threelevel Converter Comparison in Wind Power Application;
Cataliotti, A., Genduso, F., Ricco Galluzzo, G., A Space Vector Modulation Control Algorithm for VSI Multi-Level Converters;
Itoh, J., Noge, Y., Adachi, T., A Novel Five-level Threephase PWM Rectifier Using 12 Switches;
Michael, P.A., Devarajan, N., FPGA implementation of multilevel space vector PWM algorithms (2009) International Journal of Engineering and Technology, 1 (3)., August ISSN: 1793-8236;
Mailah, N.F., Neutral-point-clamped multilevel inverter using space vector modulation (2009) European Journal of Scientific Research, ISSN 1450-216X, 28 (1), pp. 82-91., EuroJournals Publishing, Inc. 2009;
Song, W.-X., Yao, G., Chen, C., Control method of threelevel neutralpoint- clamped inverter based on voltage vector diagram partition (2008) J. Shanghai Jiaotong Univ. (Sci.), 13 (4), pp. 457-461., DOI: 10.1007/s12204-008-0457-1;
Xie, X., Song, Q., Yan, G., Liu, W., MATLABbased Simulation of Three-level PWM Inverter-fed Motor Speed Control System, , Department of Electrical Engineering, Tsinghua University, Beijing 100084, P.R. China;
Kocalmiş, A., Sünter, S., Simulation of A Space Vector PWM Controller for A Three-Level Voltage-Fed Inverter Motor Drive, p. 23119., Department of Electrical and Electronic Engineering, Firat University, Elazig, TURKEY;
Halász, S., Zakharov, A., PWM Strategies of Three- Level Inverter- Fed AC Drives, Department of Electrical Machines and Drives, , Budapest University of Technology and Economics, Budapest, Hungary;
Ahmed, M.E., Mekhilef, S., Aswan Faculty of Engineering Faculty of Engineering, , University of Malaya, Malaysia, Three- Phase Three-Level Nine Switches Inverter Employing Space Vector Modulation;
Marino, R., Peresada, S., Valigi, P., Adaptive input-output linearizing control of induction motors (1993) IEEE Trans. Automat. Contr., 38 (2), pp. 208-221;
Casadei, A.T.D., Serra, G., Zarri, L., A simple method for flux weakening operation of DTC based induction motor drives (2004) ICEM, pp. 403-408;
Sullivan, C.R., Sanders, S.R., Models for induction machines with magnetic saturation of the main flux path (1995) IEEE Trans. Ind. Application., 31 (4), pp. 907-917;
Hofmann, H., Sanders, S.R., Sullivan, C.R., Stator-flux-based vector control of induction machines in magnetic saturation (1997) IEEE Trans. Ind. Applicat., 33 (4), pp. 935-941;
Kirschen, D.S., Novotny, D.W., Lipo, T.A., On-line efficiency optimization of a variable frequency induction motor drive (1985) IEEE Transactions on Industry Applications, IA-21 (3), pp. 610-616;
Tarbouchi, M., Lehuy, H., Control by exact linearization of an induction motor in field weakening regime (1998) IECON Proc., pp. 1597-1602., Aachen, Germany;
Novotnak, R.T., Chiasson, J., Bodson, M., High performance motion control of an induction motor with magnetic saturation (1999) IEEE Trans. Contr. Syst. Techn., 7 (3), pp. 315-327;
Leonard, W., (1985) Control of Electric Drives, , Berlin: Springer-Verlag;
Levi, E., A unified approach to main flux saturation modeling in d-q axis models of induction machines (1995) IEEE Trans. Ener. Conv., 10 (10), pp. 455-461;
Gokdere, L.U., (1996) Passivity-Based Methods for Control of Induction Motors, , PhD Thesis, Univ. Pittsburgh;
Nicklasson, P.J., Ortega, R., Espinosa-Perez, G., Passivity-based control of a class of Blondel-Park transformable electric machines (1997) IEEE Transactions on Automatic Control, 42 (5), pp. 629-647., PII S0018928697034351;
Abdel Fattah, H.A., Loparo, K.A., Induction motor control system performance under magnetic saturation (1999) Proceedings of the American Control Conference, pp. 1668-1672., San Diego, CA;
(2011) Simulation and Implementation of Two Level and Three-level Inverters by Matlab and Rt-lab, , abd almula g. m. gebreel, master thesis, ohio State university;
Astrom, K., Hagglund, T., (1995) PID Controllers; Theory Design and Tuning, , Instrument Society of America, Research Triangle Park;
Petrov, M., Ganchev, I., Dragotinov, I., Design aspects of fuzzy PID control (1999) International Conference on Soft Computing, Mendel "99", pp. 277-282., Brno,czech republic 9-12, jun;
Andersson, J., Applications of A Multi-objective Genetic Algorithm to Engineering Design Problems, , Springer Berlin,ISBN 0302-9743;
Dwyer, O., A. PI and PID controller tuning rules for time delay process: A summary. Part 1: PI controller tuning rules (1999) Proceedings of Irish Signals and Systems Conference, , June;
Krishnakumar, K., Goldberg David, Control system optimization using genetic algorithms (1992) Journal of Guidance, Control, and Dynamics, 15 (3), pp. 735-740;
Mahony, T.O., Downing, C.J., Fatla, K., Genetic algorithm for PID parameter optimization: Minimizing error criteria (2000) Process Control and Instrumentation 2000, pp. 148-153., 26-28 July 2000, University of Stracthclyde, July;
Varsek, A., Urbacic, T., Filipic, B., Genetic algorithms in controller design and tuning (1993) IEEE Trans. Sys.Man and Cyber, 23 (5), pp. 1330-1339;
Tokhi, M.O., Alam, M.S., Particle Swarm Optimization Algorithms and Their Application to Controller Design for Flexible Structure System;
Nedjah, N., Swarm intelligent systems Studies in Computational Intelligence, 26., Springer;
Parsopoulos, K.E., Vrahatis, M.N., Recent approaches to global optimization problems through particle swarm optimization (2002) Natural Computing, 1, pp. 235-306;
Ji, Z., Wang, Y., Chu, Y., Wu, Q., Bacterial particle swarm optimization (2009) Chinese Journal of Electronics, 18 (2)., Apr;
Rasmussen, H., (2002) Automatic Tuning of PID-regulators, , DK 9220 Aalborg, Denmark September 6
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Xiao, J., Zhou, J., Kou, P., Zhang, X., Wu, X., Li, M.
Identification of hydraulic turbine governor system based on improved unified PSO algorithm
(2012) Proceedings - 2011 2nd International Conference on Control, Instrumentation and Automation, ICCIA 2011, art. no. 6183933, pp. 159-161.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860826080&partnerID=40&md5=274e8364de411cf16ea432cbae1e1f8c
AFFILIATIONS: College of Hydroelectric Digitization Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
ABSTRACT: In this paper, we present a novel evolutionary algorithm-based approach to identification of hydraulic turbine governor system (HTGS). A new variant of particle swarm optimization (PSO) technique named unified PSO (UPSO) is employed and improved to search for optimal parameters of HTGS by minimizing errors between the model's evaluated outputs and the actual ones. The performance of the improved unified PSO (IUPSO) is compared with standard PSO and UPSO algorithms tested via numerical simulation. Identification results aptly show that the IUPSO algorithm has the advantage of convergence capability and solution quality and it provides a new way for parameter identification of hydraulic turbine governor system. © 2011 IEEE.
AUTHOR KEYWORDS: Evolutionary algorithms; Hydraulic turbine governor system; Parameter identification; Particle swarm optimization
REFERENCES: Ljung, L., Perspectives on system identification (2010) Annual Reviews in Control [J], pp. 1-12;
Yang, X.-D., Dong, C., Lu, W., Wen, J.-Y., Parameters identification for hydraulic turbine governing systems based on genetic algorithm (2005) Relay, pp. 28-30., J, 4;
Kou, P., Zhou, J., Li, C., He, Y., He, H., Identification of Hydraulic Turbine Governor System Parameters Based on Bacterial Foraging Optimization Algorithm 2010 Sixth International Conference on Natural Computation (ICNC 2010);
Li, C.-S., Zhou, J.-Z., Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm (2011) Energy Conversion and Management [J], pp. 374-381;
Clerc, M., Kenedy, J., The particle swarm- explosion, stability, and convergence in a multidimensional complex space (2002) IEEE Transactions on Evolutionary Computations [J], pp. 58-73;
He, Q., Wang, L., Liu, B., Parameter estimation for chaotic systems by particle swarm optimization (2007) Chaos, Solitons and Fractals [J], pp. 654-661;
Rasmus, K.U., Pierré, V., Parameter identification of induction motors using stochastic optimization algorithms (2004) Applied Soft Computing [J], pp. 49-64;
Sakthivel, V.P., Bhuvaneswari, R., Subramanian, S., (2010) Engineering Applications of Artificial Intelligence [J], pp. 302-312;
Parsopoulos, K.E., Vrahatis, M.N., Parameter selection and adaptation in unified paticle swarm optimization (2007) Mathematical and Computer Modelling, pp. 198-213
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Ismail, M.M.
Adaptation of PID controller using AI technique for speed control of isolated steam turbine
(2012) Proceedings of the 2012 Japan-Egypt Conference on Electronics, Communications and Computers, JEC-ECC 2012, art. no. 6186962, pp. 85-90.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860793461&partnerID=40&md5=14a2256689c20bc782c108b27bf7d364
AFFILIATIONS: Faculty of Engineering, Helwan University, Electrical Power and Machine Department, Egypt
ABSTRACT: It is known that PID controller is employed in every facet of industrial automation. The application of PID controller span from small industry to high technology industry. Tuning the parameters of a PID controller is very important in PID control. Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of a PID controller. This tuning method is very simple, but cannot guarantee to be always effective. For this reason, this paper investigates the design of self tuning for a PID controller. For this study, the model selected is of isolated turbine speed control system. Isolated turbine system means that the turbine is not connected to the grid. The reason for this is that this model is often encountered in refineries in a form of steam turbine that uses hydraulic governor to control the speed of the turbine. The PID controller of the model will be designed using the adaptive fuzzy control method and the results analyzed. The same model will be redesigned using GA, PSO and ANFIS methods. The results of design methods will be compared, analyzed and conclusion will be drawn out of the simulation made. © 2012 IEEE.
AUTHOR KEYWORDS: ANFIS; Fuzzy logic control; GA; PID controller; PSO; Self tuning controller; steam turbine
REFERENCES: Astrom, K., Hagglund, T., (1995) PID Controllers; Theory, Design and Tuning, , Instrument Society of America, Research Triangle Park;
El-Geliel, M.A., Supervisory fuzzy logic controller used for process loop control in DCS system (2003) CCA03 Conference, , Istanbul, Turkey June 23/25;
Astroum, K.J., Wittenmark, B., (1995) Adaptive Control, , Addison- Wesley;
Ibrahim, S.B.M., (2005) The Pid Controller Design Using Genetic Algorithm, , University of Southern Queensland Research Project, October;
Mendel, J.M., Fuzzy logic systems for engineering: A tutorial (1995) Proc. IEEE, 83, pp. 345-377;
Wang, L.X., (1994) Adaptive Fuzzy System & Control Design & Stability Analysis, , Prentice-Hall;
Li, H.-X., Tso, S.K., Quantitative design and analysis of fuzzy proportional-integral- derivatiwe control - A step towards autotuning (2000) International Journal of Systems Science, 31 (5), pp. 545-553., DOI 10.1080/002077200290867;
Pattaradej, T., Chen, G., Sooraksa, P., Design and implementation of fuzzy P2ID control of a bicycle robot (2002) Integrated Computer-aided Engineering, 9 (4);
Tang, W., Chen, G., Lu, R., A modified fuzzy PI controller for a flexible-jonit robot arm with uncertainties (2001) Fuzzy Setd and System, 118, pp. 109-119;
Reznik, L., Ghanayem, O., Bourmistrov, A., PID pulse fuzzy controller structures as a design for industrial application (2002) Engineering Application of Artificial Intelligence, 13 (4), pp. 419-430;
Passino, K.M., Yurkovich, S., (1998) Fuzzy Control, , Addison Wesley, longnan, Inc;
Chen, G.R., Pham, T.T., (2000) Introduction to Fuzzy Sets, Fuzzy Logic, Fuzzy Control System, , CRC.Press,Boac Raton,FL,USA;
Petrov, M., Ganchev, I., Dragotinov, I., Design aspects of fuzzy PID control (1999) International Conference on Soft Computing, Mendel "99", pp. 277-282., Brno,czech republic 9-12, jun;
Andersson, J., Applications of A Multi-objective Genetic Algorithm to Engineering Design Problems, , Springer Berlin,ISBN 0302-9743;
Reeves, C.R., Rowe, J.E., (2002) Genetic Algorithm Principles and Perspective, A Guide to GA Theory, , Kluwer Academic PublishersISBN 1-4020-7240-6;
Bandyopadhyay, R., Chakraborty, U.K., Patranabis, D., Autotuning a PID controller: A fuzzy-genetic approach (2001) Journal of Systems Architecture, 47 (6), pp. 663-673., PII S1383762101000224;
Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning, , Addison-Wesley Pub. Co;
Wang, Q., Spronck, P., Figure 10 genetic algorithm process flow chart ering problems (2003) Proceedings of the Second Conference on Machine Learning and Cybernetics;
Tokhi, M.O., Alam, M.S., Particle Swarm Optimization Algorithms and Their Application to Controller Design for Flexible Structure System;
Nedjah, N., Swarm intelligent systems Studies in Computational Intelligence, 26., Springer;
Parsopoulos, K.E., Vrahatis, M.N., Recent approaches to global optimization problems through particle swarm optimization (2002) Natural Computing, 1, pp. 235-306;
Ji, Z., Wang, Y., Chu, Y., Wu, Q., Bacterial particle swarm optimization (2009) Chinese Journal of Electronics, 18 (2)., Apr
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Ludwig, S.A.
Applying particle swarm optimization to quality-of-service-driven web service composition
(2012) Proceedings - International Conference on Advanced Information Networking and Applications, AINA, art. no. 6184926, pp. 613-620.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860733526&partnerID=40&md5=5e86c264bed7923e2b76c7fd57cad4c6
AFFILIATIONS: Department of Computer Science, North Dakota State University, Fargo, ND, United States
ABSTRACT: Web service composition is a very important task in service-oriented environments. The composition of services has to be based not only on functional but also on non-functional properties. In particular, the selection of the services should be performed at run-time rather than at design-time in order to adjust to changes in the environment that are due to the volatile nature of service-oriented environments. An optimization technique is necessary to perform the composition of web services based on Quality of service (QoS) parameters. Many different methods have been used to address the service composition problem, in particular, many linear programming methods have been used to optimize the process of service composition. The proposed work is different from related work in two aspects: (1) two meta-heuristic methods based on Particle Swarm Optimization (PSO) are introduced to address the optimization problem, and (2) several workflow requests are being processed simultaneously. The experimental results show that the hybrid PSO method in particular performs very well in service-oriented environments. © 2012 IEEE.
AUTHOR KEYWORDS: particle swarm optimization; quality of service; workflow composition
REFERENCES: Overview, , http://tab.computer.org/tcsc/scope.htm, IEEE Computer Society;
Papazoglou, M.P., Georgakopoulos, D., Service-oriented computing (2003) Communications of the ACM, 46 (10), pp. 25-65;
Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F., Service- oriented computing: State of the art and research challenges (2007) Computer, pp. 38-45., November;
Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F., Service-oriented computing: State of the art and research challenges (2007) IEEE Computer, 40 (11), pp. 38-45;
Kavantzas, N., Burdett, D., Ritzinger, G., Fletcher, T., Lafon, Y., Barreto, C., (2005) Web Services Choreography Description Language Version 1.0, , World Wide Web Consortium, Candidate Recommendation CRws- cdl- 10-20051109, November;
(2006), http://www.oasisopen.org/committees/, Web Services Composite Application Framework. Internet(2007), http://www.osoa.org/display/Main/Service+Component+Architecture+Home, O. S. Collaboration. InternetMajithia, S., Walker, D.W., Gray, W.A., A framework for automated service composition in service-oriented architectures (2004) ESWS, pp. 269-283., C. Bussler, J. Davies, D. Fensel, and R. Studer, editors. volume 3053 of Lecture Notes in Computer Science. Springer;
Berardi, D., Calvanese, D., Giacomo, G.D., Lenzerini, M., Mecella, M., Automatic service composition based on behavioral descriptions (2005) Int. J. Cooperative Inf. Syst., 14 (4), pp. 333-376;
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Akkiraju, R., Verma, K., Goodwin, R., Doshi, P., Lee, J., Executing abstract web process flows (2004) International Conference on Automated Planning and Scheduling (ICAPS);
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Schuller, D., Eckert, J., Miede, A., Schulte, S., Steinmetz, R., QoSAware service composition for complex workflows (2010) Proceedings of the 2010 Fifth International Conference on Internet and Web Applications and Services;
Comuzzi, M., Pernici, B., A framework for QoS-based Web service contracting (2009) ACM Trans. Web, 3 (3);
Hwang, S.-Y., Lim, E.-P., Lee, C.-H., Chen, C.-H., Dynamic web service selection for reliable web service composition (2008) IEEE Transactions on Services Computing, pp. 104-116., April-June;
Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., Sheng, Q.Z., Quality driven web services composition (2003) Proceedings of the 12th International Conference on World Wide Web;
Harrison, A., Wang, I., Taylor, I., Shields, M., WS-RF workflow in triana (2007) International Journal of High Performance Computing Applications (IJHPCA) Special Issue on Workflow Systems in Grid Environments;
Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Proceedings of IEEE International Conference on Neural Networks;
Clerc, M., Discrete particle swarm optimization - Illustrated by the traveling salesman problem (2004) New Optimization Techniques in Engineering, , Springer;
Parsopoulos, K.E., Vrahatis, M.N., Recent approaches to global optimization problems through particle swarm optimization (2002) Natural Computing, 1, pp. 235-306;
Clerc, M., Kennedy, J., The particle swarm explosion, stability, and convergence in a multidimensional complex space (2002) IEEE Transactions on Evolutionary Computation, 6, pp. 58-73;
Trelea, I.C., The particle swarm optimization algorithm: Convergence analysis and parameter selection (2003) Information Processing Letters, 85, pp. 317-325;
Michalewicz, Z., Fogel, D.B., (2002) How to Solve It: Modern Heuristics, , Springer-Verlag;
Zhanga, J.-R., Zhanga, J., Lokc, T.-M., Lyud, M.R., A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training (2007) Journal of Applied Mathematics and Computation, 185 (2), pp. 1026-1037., 15 February;
Kim, D.H., Abraham, A., Hirota, K., Hybrid genetic: Particle swarm optimization algorithm (2007) Journal of Hybrid Evolutionary Algorithms in Studies in Computational Intelligence, 75, pp. 147-170., 2007;
Sivanandam, S.N., Visalakshi, P., Bhuvaneswari, A., Multiprocessor scheduling using hybrid particle swarm optimization with dynamically varying inertia (2007) IJCSA, 4 (3), pp. 95-106;
Kuhn, H.W., The Hungarian method for the assignment problem (1955) Naval Research Logistics, 52 (1);
Kuhn, H.W., The Hungarian method for solving the assignment problem (1955) Naval Research Logistics Quarterly, 2, p. 83;
Munkres, J., Algorithms for the assignment and transportation problems (1957) Journal of the Society for Industrial and Applied Mathematics, 5, p. 32;
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http://en.wikipedia.org/wiki/Hungarian_algorithm, Hungarian Algorithm, last retrieved December 2011Nedas, K., Munkres' (Hungarian) Algorithm, , http://konstantinosnedas.com/dev/soft/munkres.htm, Java implementation, last retrieved on December 2011 from
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Costa, L.a , Espírito Santo, I.A.C.P.a , Fernandes, E.M.G.P.b
A hybrid genetic pattern search augmented Lagrangian method for constrained global optimization
(2012) Applied Mathematics and Computation, 218 (18), pp. 9415-9426.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860470150&partnerID=40&md5=0b9c6274310fb3e4a39c504ce48bda26
AFFILIATIONS: Department of Production and Systems, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;
Algoritmi R and D Center, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
ABSTRACT: Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems. Thus, in order to reduce the total number of function evaluations, local and global techniques may be combined. Moreover, the hybridization may provide a more effective trade-off between exploitation and exploration of the search space. In this study, we propose a new hybrid genetic algorithm based on a local pattern search that relies on an augmented Lagrangian function for constraint-handling. The local search strategy is used to improve the best approximation found by the genetic algorithm. Convergence to an ε-global minimizer is proved. Numerical results and comparisons with other stochastic algorithms using a set of benchmark constrained problems are provided. © 2012 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Augmented Lagrangian; Genetic algorithm; Global optimization; Pattern search
REFERENCES: Beyer, H.-G., Sendhoff, B., Robust optimization - A comprehensive survey (2007) Computer Methods in Applied Mechanics and Engineering, 196 (33-34), pp. 3190-3218., DOI 10.1016/j.cma.2007.03.003, PII S0045782507001259;
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Coello Coello, C.A., Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art (2002) Computer Methods in Applied Mechanics and Engineering, 191 (11-12), pp. 1245-1287., DOI 10.1016/S0045-7825(01)00323-1, PII S0045782501003231;
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Hedar, A.-R., Fukushima, M., Heuristic pattern search and its hybridization with simulated annealing for nonlinear global optimization (2004) Optimization Methods and Software, 19, pp. 291-308;
Hedar, A.-R., Fukushima, M., Derivative-free filter simulated annealing method for constrained continuous global optimization (2006) Journal of Global Optimization, 35 (4), pp. 521-549., DOI 10.1007/s10898-005-3693-z;
Hooke, R., Jeeves, T.A., Direct search solution of numerical and statistical problems (1961) Journal on Associated Computation, 8, pp. 212-229;
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Lemonge, A.C.C., Barbosa, H.J.C., An adaptive penalty scheme for genetic algorithms in structural optimization (2004) International Journal for Numerical Methods in Engineering, 59 (5), pp. 703-736., DOI 10.1002/nme.899;
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Lewis, R.M., Torczon, V., A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds (2002) SIAM Journal on Optimization, 12, pp. 1075-1089;
Liu, J.-L., Lin, J.-H., Evolutionary computation of unconstrained and constrained problems using a novel momentum-type particle swarm optimization (2007) Engineering Optimization, 39 (3), pp. 287-305., DOI 10.1080/03052150601131000, PII 777462353;
Michalewicz, Z., Genetic algorithms, Numerical optimization and constrains (1995) Proceedings of 6th International Conference on Genetic Algorithms, pp. 151-158;
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Runarsson, T.P., Yao, X., Search biases in constrained evolutionary optimization (2005) IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 35 (2), pp. 233-243., DOI 10.1109/TSMCC.2004.841906;
Shang, W., Zhao, S., Shen, Y., A flexible tolerance genetic algorithm for optimal problems with nonlinear equality constraints (2009) Advanced Engineering Informatics, 23, pp. 253-264;
Tahk, M.-J., Woo, H.-W., Park, M.-S., A hybrid optimization method of evolutionary and gradient search (2007) Engineering Optimization, 39 (1), pp. 87-104., DOI 10.1080/03052150600957314, PII P4UW662641228HH4;
Torczon, V., On the convergence of pattern search algorithms (1997) SIAM Journal on Optimization, 7, pp. 1-25;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Mandal, M., Mukhopadhyay, A.
A multiobjective PSO-based approach for identifying non-redundant gene markers from microarray gene expression data
(2012) 2012 International Conference on Computing, Communication and Applications, ICCCA 2012, art. no. 6179219, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860630865&partnerID=40&md5=b3eb3d3aad838daa368695be02ccb0b1
AFFILIATIONS: Department of Computer Science and Engineering, University of Kalyani, Kalyani-74123 Nadia, West Bengal, India
ABSTRACT: Cancer is a phenotypic complexity which affects genes, proteins, pathways and regulatory networks. The research is still in progress to identify the important genes which are responsible for various types of cancer. In this context important genes refers to the gene marker which indicates change in expression or state of protein that correlates with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. However extracting these marker genes from a huge set of genes is a major problem. There are many approaches for detecting these informative genes. Most of the approaches can find a set of redundant marker genes. Motivated by this fact a multiobjective optimization method has been proposed which can find small set of non-redundant disease related genes which have high sensitivity, specificity and accuracy at the same time. In this article the optimization problem has been modeled as multiobjective problem based on the framework of particle swarm optimization. Using the real life datasets, performance of proposed algorithm has been compared with other different techniques. © 2012 IEEE.
REFERENCES: Alon, U., Barka, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J., Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays (1999) Proceedings of the National Academy of Sciences of the United States of America, 96 (12), pp. 6745-6750., DOI 10.1073/pnas.96.12.6745;
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Cui, X., Potok, T.E., Document clustering analysis based on hybrid pso+k-means algorithm (2005) Journal of Computer Sciences, (27-33);
Cui, X., Potok, T.E., Document clustering using particle swarm optimization (2005) IEEE Swarm Intelligence Symposium, Pasadena, California, 21 (24), pp. 4356-4362;
Deb, K., Multi-objective optimization using evolutionary algorithms (2001) England: John Wiley and Sons, Ltd, 6, pp. 182-197;
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Sierra, M.R., Coello Coello, C.A., Multi-objective particle swarm optimizers: A survey of the state-of-The-art (2006) International Journal of Computational Intelligence Research, 2 (3), pp. 287-308;
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Zitzler, E., Thiele, L., An evolutionary algorithm for multiobjective optimization: The strength pareto approach (1998) Tech. Rep. 43, Gloriastrasse 35, CH-8092 Zurich, Switzerland, 1 (3), pp. 129-156;
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Maulik, U., Mukhopadhyay, A., Bandyopadhyay, S., Combining paretooptimal clusters using supervised learning for identifying coexpressed genes (2009) BMC Bioinformatics, 10 (27)
DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Ruiz, R., Sanchez, E.N., Loukianov, A.G.
Neural backstepping control: Green energy applications
(2012) 2012 IEEE 3rd Latin American Symposium on Circuits and Systems, LASCAS 2012 - Conference Proceedings, art. no. 6180328, .
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860486225&partnerID=40&md5=dd96a44f896a7b2478fa07802d6dd444
ABSTRACT: In this paper, the authors present a discrete-time adaptive neural backstepping control for a doubly fed induction generator (DFIG), based on a discrete-time high order neural network (HONN), which is trained with a new particle swarm optimization extended Kalman filter (PSOEKF) algorithm. The discrete-time adaptive neural backstepping control performance is illustrated via simulations. © 2012 IEEE.
AUTHOR KEYWORDS: Doubly Fed Induction Generator; Grid Side Converter; Inverse Optimal Control; Particle Swarm Optimization; Rotor Side Converter; Wind Energy
REFERENCES: Pea, R., Clare, J.C., Asher, G.M., Doubly fed induction generator using back to back pwm converters and its application to variable speed wind energy generation (1996) IEEE Proceedings Electric Power Applications, 143 (3), pp. 231-241., May;
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Qiao, W., Venayagamoorthy, G.K., Harley, R.G., Design of optimal pi controllers for doubly fed induction generators driven by wind turbines using particle swarm optimization (2006) International Joint Conference on Neural Networks, pp. 1982-1987., July;
Ruiz, R., Sánchez, E.N., Loukianov, A.G., Discrete-time adaptive neural backstepping control for a double fed induction generator (2009) International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1-5., November;
Sanchez, E.N., Alanis, A.Y., Loukianov, A.G., (2008) Discrete-Time High Order Neural Control, , Germany: Springer Verlag;
Ricalde, L.J., Sanchez, E.N., Inverse optimal nonlinear high order recurrent neural observe International Joint Conference on Neural Networks, August 2005;
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Ruiz, R., Sánchez, E.N., Loukianov, A.G., Discrete time block control of a double fed induction generator using sliding modes IEEE Multi-conference on Systems and Control (MSC), July 2009;
Tapia, A., Tapia, G., Estolaza, J.X., Saenz, J.R., Modeling and control of a wind turbine driven doubly fed induction generator (2003) IEEE Transactions on Energy Conversion, 26 (2), pp. 194-204., June;
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Zhang, H.-F.a b , Liang, G.-Q.a , Zhang, J.a
Evaluation method of high-tech knowledge innovation based on particle swarm optimization fuzzy neural networks
(2012) Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 34 (5), pp. 973-976.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863515703&partnerID=40&md5=6fff4dbc1ce2ba97b93081aab8a9a14e
AFFILIATIONS: School of Management, Northwestern Polytechnical University, Xi'an 710072, China;
Beijing Institute of Aerospace Information, Beijing 100854, China
ABSTRACT: According to the characteristic of nonlinearity, uncertainty, time variation, this paper presents high-tech knowledge innovation capacity evaluation index system, and puts forward an improved fuzzy neural network evaluation model combined with particle swarm optimization. This model can combine multiple concurrent time-varying fuzzy neural network algorithm and realize network of learning and accurate reasoning, by evolution preset network connection weights, threshold and compensation parameters with particle swarm optimization. Through simulating application, it has been proved that this model structure and the algorithm are feasible and facilitate for computer implementation, and get the overall convergence speed and generalization ability, convergence precision of superior original learning algorithm.
AUTHOR KEYWORDS: Evaluation method; Fuzzy neural network; High-tech knowledge innovation; Partical swarm optimization (PSO)
REFERENCES: Cheng, W., Han, L.Y., Framework of knowledge management system based on knowledge flow in areospace enter-prise (2006) Systems Engineering and Electronics, 28 (11), pp. 1675-1678;
Sun, Y.W., Wei, Y.P., Study on knowledge diffusion of high-tech enterprise alliance from the small-word network perspective (2011) Journal of Management Sciences in China, 14 (12), pp. 17-26;
Wang, K., Yuan, J., Hot spots analysis of information evaluation and relevant enlightenment (2012) Technoeconomics & Management, (2), pp. 35-38;
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Shantz, J.R., Use of knowledge management as a learning transfer platform (2003), pp. 5-6., San Diego, California: Alliant International UniversityKennedy, J., Eberhart, R.C., Particle swarm optimization (1995) Proc. of the IEEE International Conference on Neural Networks, pp. 1942-1948;
Lavrac, N., Ljubic, P., Urbancic, T., Trust modeling for networked organizations using reputation and collaboration estimates (2007) IEEE Trans. on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 37 (3), pp. 429-439;
Chang, L.K., Sangjae, L., Kang, I.W., KMPI: Measuring know-ledge management performance (2005) Information and Management, 42 (3), pp. 469-482;
Tranfield, D., Young, M., Partingtond, D., Knowledge management routines for innovation projects: Developing a hierarchical process model (2003) International Journal of Innovation Management, 7 (1), pp. 27-49;
van den Bergh, F., Engelbrecht, A.P., A study of particle swarm optimization particle trajectories (2005) Information Sciences, 8 (6), pp. 243-267;
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Liu, W.B., Wang, X.J., Study on evolutionary games based on PSO-neural networks (2007) Systems Engineering and Electronics, 29 (8), pp. 1282-1284;
Samaddar, S., Kadiyala, S.S., An analysis of interorganizational resource sharing decisions in collaborative knowledge creation (2006) European Journal of Operational Research, 170 (1), pp. 192-210;
Shi, Y.H., Eberhart, R.C., Chen, Y., Implementation of evolutionary fuzzy systems (1999) IEEE Trans. on Fuzzy Systems, 7 (2), pp. 109-119;
Parsopoulos, K.E., Vrahatis, M.N., Recent approaches to global optimization problems through particle swarm optimization (2002) Natural Computing, 1 (2), pp. 235-306;
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Wu, Y.X., Qin, X.S., Zhang, H.F., Development decision evaluation method of complex product based on fuzzy neural network (2011) Systems Engineering and Electronics, 33 (7), pp. 1575-1579
DOCUMENT TYPE: Article
SOURCE: Scopus
Cui, Z.a b , Yang, C.a , Sanyal, S.c
Training artificial neural networks using APPM
(2012) International Journal of Wireless and Mobile Computing, 5 (2), pp. 168-174.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860773484&partnerID=40&md5=e608915ae3767abeda444db3abc412a1
AFFILIATIONS: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, 030024, China;
State Key Laboratory of Novel Software Technology, Nanjing University, 210093, China;
School of Technology and Computer Science, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai-400005, India
ABSTRACT: In order to train Artificial Neural Networks (ANNs), we used a new stochastic optimisation algorithm that simulates the plant growing process. It designs an artificial photosynthesis operator and an artificial phototropism operator to mimic photosynthesis and phototropism mechanisms, we call it briefly APPM algorithm. In this algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Phototropism operator is designed to introduce the fitness function value, and it is also used to decide the growing direction. In this paper, we apply APPM algorithm to train the connection weights for ANN. To assess the performance of our APPM-trained ANN (APPMANN), two realworld problems, named cleveland heart disease classification problem and sunspot number forecasting problem, are adopted. Simulation results show that APPMANN increases the performance significantly when compared with other sophisticated machine learning techniques proposed in recent years. Copyright © 2012 Inderscience Enterprises Ltd.
AUTHOR KEYWORDS: Artificial neural networks; Photosynthesis operator; Phototropism operator
REFERENCES: Boryczka, M., Slowinski, R., Derivation of optimal decision algorithms from decision tables using rough sets (1988) Bulletin of the Polish Academy of Sciences: Series Technical Sciences, 36, pp. 252-260;
Bryant, D.A., Frigaard, N.-U., Prokaryotic photosynthesis and phototrophy illuminated (2006) Trends in Microbiology, 14 (11), pp. 488-496., DOI 10.1016/j.tim.2006.09.001, PII S0966842X06002265;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Shakya, S.a , Kern, M.a , Owusu, G.a , Chin, C.M.b
Neural network demand models and evolutionary optimisers for dynamic pricing
(2012) Knowledge-Based Systems, 29, pp. 44-53.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857372564&partnerID=40&md5=88891c7a9fc4e218473686df49fae7d3
AFFILIATIONS: Business Modelling and Operational Transformation Practice, BT Innovate and Design, Ipswich IP5 3RE, United Kingdom;
Core Design Team, BT Innovate and Design, Ipswich IP5 3RE, United Kingdom
ABSTRACT: Dynamic pricing is a pricing strategy where price for the product changes according to the expected demand for it. Some work on using neural network for dynamic pricing have been previously reported, such as for forecasting the demand and modelling consumer choices. However, little work has been done in using them for optimising pricing policies. In this paper, we describe how neural networks and evolutionary algorithms can be combined together to optimise pricing policies. Particularly, we build a neural network based demand model and use evolutionary algorithms to optimise policy over build model. There are two key benefits of this approach. Use of neural network makes it flexible enough to model a range of different demand scenarios occurring within different products and services, and the use of evolutionary algorithm makes it versatile enough to solve very complex models. We also evaluate the pricing policies found by neural network based model to that found by other widely used demand models. Our results show that proposed model is more consistent, adapts well in a range of different scenarios, and in general, finds more accurate pricing policy than other three compared models. © 2011 Elsevier B.V. All rights reserved.
AUTHOR KEYWORDS: Dynamic pricing; Evolutionary computation; Neural networks; Price optimisation; Revenue management
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Fan, L., Wang, Y.
A minimum-elimination-escape memetic algorithm for global optimization: MEEM
(2012) International Journal of Innovative Computing, Information and Control, 8 (5 B), pp. 3689-3703.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860894187&partnerID=40&md5=b61d1a9fa1a4b9f8e6244e121e090b3d
AFFILIATIONS: School of Computer Science and Technology, Xidian University, No. 2, South Taibai Road, Xi'an 710071, China
ABSTRACT: Smoothing function method and filled function method are two of the most efficient methods for global optimization problems. The former can eliminate many local minima during the optimization process, but it often loses some useful information on looking for descent directions. The later can escape from local minima and find a better minimum, but it is usually parameter sensitive. To overcome these shortcomings, an auxiliary function is designed which integrates the advantages of both smoothing function and filled function; that is, it not only can eliminate many local minima and escape from local minima, but also cannot lose the useful information and is not parameter sensitive. By using such a function, many local minima will be eliminated and the algorithm successively goes from one local minimum to another better local minimum during optimization process, and finds the global minimum finally. To enhance the efficiency of the algorithm, a local search called square search is designed and integrated into the algorithm. Based on these techniques, a minimum-elimination-escape memetic algorithm called MEEM is proposed in this paper. The simulations are made on 30 standard benchmark problems and the performance of the proposed algorithm is compared with that of some well performed existing algorithms. The results indicate the performance of the proposed algorithm is more effective. © 2012 ICIC International.
AUTHOR KEYWORDS: Global optimization; Memetic algorithm; Minimum elimination; Smoothing function; Square search
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DOCUMENT TYPE: Article
SOURCE: Scopus
Qin, H., Wan, Y., Zhang, W., Song, Y.
Aberration correction of single aspheric lens with particle swarm algorithm
(2012) Jisuan Wuli/Chinese Journal of Computational Physics, 29 (3), pp. 426-432.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863603041&partnerID=40&md5=e50b4bdc218354393a83fd29e013f302
AFFILIATIONS: Department of Sciences, Shandong University of Technology, Zibo 255049, China
ABSTRACT: Automatic design and analysis of a single aspheric lens using particle swarm algorithm is presented. Particle swarm algorithm is applied to aberration correction of a single aspheric lens to meet requirements of spherical aberration. A mathematical model is constructed. And a program code is developed. Merit functions in an optical system are employed as fitness functions, which combined coefficients of a higher degree polynomial equation, a reciprocal of radius of curvature, a conic constant, thicknesses among lens surfaces and refractive indices regarding an optical system. Automatic correction of spherical aberration is performed with the function. An example shows that PSO as a tool for spherical aberration correction of a single aspheric lens is simple and effective.
AUTHOR KEYWORDS: Aspheric lens; Fitness function; Optical design; Particle swarm optimization; Spherical aberration correction
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Zhang, L., Wang, Y., Li, L., Genetic algorithm applied to automatic lens design (2002) Acta Optica Sinica, 22 (1), pp. 74-78;
Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Proceedings of the 4th IEEE International Conference on Neural Networks, pp. 1942-1948., Piscataway: IEEE Service Center;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Pontani, M.a , Conway, B.A.b
Particle swarm optimization applied to impulsive orbital transfers
(2012) Acta Astronautica, 74, pp. 141-155.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857647880&partnerID=40&md5=2c7a5f434c8c3536f3b11d232cbe5800
AFFILIATIONS: Scuola di Ingegneria Aerospaziale, University of Rome la Sapienza, Rome, Italy;
Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
ABSTRACT: The particle swarm optimization (PSO) technique is a population-based stochastic method developed in recent years and successfully applied in several fields of research. It mimics the unpredictable motion of bird flocks while searching for food, with the intent of determining the optimal values of the unknown parameters of the problem under consideration. At the end of the process, the best particle (i.e. the best solution with reference to the objective function) is expected to contain the globally optimal values of the unknown parameters. The central idea underlying the method is contained in the formula for velocity updating. This formula includes three terms with stochastic weights. This research applies the particle swarm optimization algorithm to the problem of optimizing impulsive orbital transfers. More specifically, the following problems are considered and solved with the PSO algorithm: (i) determination of the globally optimal two- and three-impulse transfer trajectories between two coplanar circular orbits; (ii) determination of the optimal transfer between two coplanar, elliptic orbits with arbitrary orientation; (iii) determination of the optimal two-impulse transfer between two circular, non-coplanar orbits; (iv) determination of the globally optimal two-impulse transfer between two non-coplanar elliptic orbits. Despite its intuitiveness and simplicity, the particle swarm optimization method proves to be capable of effectively solving the orbital transfer problems of interest with great numerical accuracy. © 2011 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Globally optimal orbital transfers; Heuristic optimization methods; Swarming theory
REFERENCES: Engelbrecht, A.P., (2007) Computational Intelligence. An Introduction, pp. 285-411., 2nd ed. Wiley Chichester, UK 555559;
Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Proceedings of the IEEE International Conference on Neural Networks, p. 19421948., Piscataway, NJ;
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Hu, X., Eberhart, R., Solving constrained nonlinear optimization problems with particle swarm optimization (2002) Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics (SCI 2002), , Orlando, FL;
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Hu, X., Eberhart, R.C., Shi, Y., Engineering optimization with particle swarm (2003) Proceedings of the IEEE Swarm Intelligence Symposium (SIS 2003), p. 243246., Indianapolis, IN;
Eberhart, R.C., Shi, Y., Comparing inertia weights and constriction factors in particle swarm optimization (2000) Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2000), p. 8488., La Jolla, CA;
Carlisle, A., Dozier, G., An off-the-shelf PSO (2001) Proceedings of the Workshop on Particle Swarm Optimization, p. 16., Indianapolis, IN;
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Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method for constrained optimization problems, intelligent technologies - Theory and applications: New trends in intelligent technologies (2002) Frontiers in Artificial Intelligence and Applications Series, 76, pp. 214-220., P. Sincak, J. Vascak, V. Kvasnicka, J. Pospichal;
Higashi, N., Iba, H., Particle swarm optimization with Gaussian mutation (2003) Proceedings of the IEEE Swarm Intelligence Symposium (SIS 2003), p. 7279., Indianapolis, IN;
Angeline, P.J., (1998) Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences, Evolutionary Programming VII, Lecture Notes in Computer Science, 1447, p. 601610., Springer;
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Hassan, B.R., Weck De, C.O., A Comparison of particle swarm optimization and the genetic algorithm (2005) Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, , Austin, TX, AIAA paper 2005-1897;
Pontani, M., Conway, B.A., Particle swarm optimization applied to space trajectories (2010) J. Guidance Control Dyn., 33 (5), pp. 1429-1441;
Pontani, M., Conway, B.A., (2010) Swarming Theory Applied to Space Trajectory Optimization, Spacecraft Trajectory Optimization, p. 263293., Cambridge University Press;
Bessette, C.R., Spencer, D.B., Optimal Space Trajectory Design: A Heuristic-Based Approach (2006) Advances in the Astronautical Sciences, 124, p. 16111628., Univelt Inc., San Diego, CA AAS paper 06-197;
Bessette, C.R., Spencer, D.B., Identifying optimal interplanetary trajectories through a genetic approach (2006) Proceedings of the AIAA/AAS Astrodynamics Specialist Conference and Exhibit, , Keystone, CO, AIAA paper 2006-6306;
Spaans, C.J., Mooij, E., Performance evaluation of global trajectory optimization methods for a solar polar sail mission (2009) Proceedings of the AIAA Guidance, Navigation, and Control Conference, , Chicago, IL, AIAA paper 2009-5666;
Vasile, M., Minisci, E., Locatelli, M., On testing global optimization algorithms for space trajectory design (2008) Proceedings of the AIAA/AAS Astrodynamics Specialist Conference and Exhibit, , Honolulu, HI, AIAA paper 2008-6277;
Zhu, K., Li, J., Baoyin, H., Satellite scheduling considering maximum observation coverage time and minimum orbital transfer fuel cost (2010) Acta Astronaut., 66, pp. 220-229;
Zhu, K., Jiang, F., Li, J., Baoyin, H., Trajectory optimization of multi-asteroids exploration with low thrust (2009) Trans. Jpn. Soc. Aeronaut. Space Sci., 52 (175), pp. 47-54;
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Pontani, M., Simple method to determine globally optimal orbital transfers (2009) J. Guidance Control Dyn., 32 (3), pp. 899-914;
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Stratemeier, D., Optimum two-impulse orbital transfer solved using evolutionary programming (2002) Proceedings of the AIAA/AAS Astrodynamics Specialist Conference and Exhibit, , Monterey, CA, AIAA paper 2002-4908
DOCUMENT TYPE: Article
SOURCE: Scopus
Awwad, O.a , Al-Fuqaha, A.b , Khan, B.c , Brahim, G.B.d
Topology control schema for better QoS in hybrid RF/FSO mesh networks
(2012) IEEE Transactions on Communications, 60 (5), art. no. 6198405, pp. 1398-1406.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861149847&partnerID=40&md5=879de69f67805cbfb333d55de97f3a1c
AFFILIATIONS: Quest Software, Toronto, ON M5A 4L5, Canada;
Computer Science Department, Western Michigan University, Kalamazoo, MI 49008, United States;
John Jay College, City University of New York, New York, NY 10019, United States;
Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia
ABSTRACT: The practical limitations and challenges of radio frequency (RF) based communication networks have become increasingly apparent over the past decade, leading researchers to seek new hybrid communication approaches. One promising strategy that has been the subject of considerable interest is the augmentation of RF technology by Free Space Optics (FSO), using the strength of each communication technology to overcome the limitations of the other. In this article, we introduce a new scheme for controlling the topology in hybrid Radio-Frequency/Free Space Optics (RF/FSO) wireless mesh networks. Our scheme is based on adaptive adjustments to both transmission power (of RF and FSO transmitters) and the optical beam-width (of FSO transmitters) at individual nodes, with the objective of meeting specified Quality of Service (QoS) requirements, specifically end-to-end delay and throughput. We show how one can effectively encode the instantaneous objectives and constraints of the system as an instance of Integer Linear Programming (ILP). We demonstrate that the technique of Lagrangian Relaxation (LR), augmented with iterative repair heuristics, can be used to determine good (albeit sub-optimal) solutions for the ILP problem, making the approach feasible for mid-sized networks. We make the proposed scheme viable for large-scale networks in terms of number of nodes, number of transceivers, and number of source-destination pairs by solving the ILP problem using a Particle Swarm Optimization (PSO) implementation. © 2012 IEEE.
AUTHOR KEYWORDS: Hybrid RF/FSO; Lagrangian relaxation; Linear programming; MANETs; Particle swarm optimization; QoS; Topology control
REFERENCES: Willebrand, H., Ghuman, B.S., (2001) Free Space Optics, , 1st edition.Sams Pubs;
http://www.bluehaze.com.au/modlight/Nichols, R., Protocol adaptation in hybrid RF/optical wireless networks Proc. 2005 IEEE MILCOM;
Milner, S., Davis, C., Hybrid free space optical/RF networks for tactical operations Proc. 2004 IEEE MILCOM;
Juarez, J., Dwivedi, A., Hammons, A., Jones, S., Weerackody, V., Nichols, R., Free space optical communications for next-generation military networks (2006) IEEE Commun. Mag, , Nov;
Izadpanah, H., Elbatt, T., Kukshya, V., Dolezal, F., Ryu, B.K., Highavailability free space optical and RF hybrid wireless networks (2003) IEEE Wireless Netw., 10 (2), pp. 45-53;
Akella, J., Liu, C., Partyka, D., Yuksel, M., Kalyanaraman, S., Dutta, P., Building blocks for mobile free-space-optical networks Proc. 2005 IFIP Int. Conf. Wireless Optical Commun. Netw;
Derenick, J., Thorne, C., Spletzer, J., On the deployment of a hybrid freespace optic/radio frequency (FSO/RF) mobile ad-hoc network Proc. 2005 IEEE/RSJ Int. Conf. Intelligent Robots Syst;
Ramanathan, R., Hain, R., Topology control of multihop wireless networks using transmit power adjustment Proc. 2000 IEEE INFOCOM;
Cayang, M., Topology, control in ad hoc wireless networks using cooperative communication (2006) IEEE Trans. Mobile Computing, 5 (6)., June;
Kashyap, A., Rawat, A., Shayman, M., Integrated backup topology control and routing of obscured traffic in hybrid RF/FSO networks Proc. 2006 IEEE Globecom;
Kashyap, A., Lee, K., Kalantari, M., Khuller, S., Shayman, M., Integrated topology control and routing in wireless optical mesh networks (2007) Computer Netw. J., 51, pp. 4237-4251., Oct;
Dong, Q., Banerjee, S., Minimum energy reliable paths using unreliable wireless links Proc. 2005 MobiHoc;
Proakis, J.G., (2001) Digital Communications, , McGraw Hill;
Ben Brahim, G., Awwad, O., Al-Fuquaha, A., Khan, B., Kountanis, D., Guizani, M., A new MILP formulation of a new budgeted location-based cooperative model for MANETs Proc. 2007 IEEE Globecom;
Jaime, L., Aniket, D., Eswaran, B., Stuart, M., Christopher, D., Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios (2005) Proc. SPIE, 5892, pp. 52-60;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Vitela, J.E., Castaños, O.
A sequential niching memetic algorithm for continuous multimodal function optimization
(2012) Applied Mathematics and Computation, 218 (17), pp. 8242-8259.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859419423&partnerID=40&md5=3feddd81e128758d7c79079732827f34
AFFILIATIONS: Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, 04510 México D.F., Mexico
ABSTRACT: This work discusses a sequential niching algorithm for multiple optimal determination. The procedure consists of a sequence of genetic algorithms (GA) runs, which incorporate a gradient-based hill-climbing algorithm, and make use of a derating function and of niching and clearing techniques to promote the occupation of different niches in the function to be optimized. Thus, the algorithm searches the solution space eliminating from the fitness landscape previously located peaks forcing the individuals to converge into unoccupied niches. An effective search of the solution space is stimulated incorporating in the algorithm stages dedicated to find new promising domains in the variable space and stages that exploits the located promising regions. Unlike other algorithms the efficiency of the sequential niching memetic algorithm (SNMA) proposed in this work is not highly sensitive to the niche radius. Performance measurements with 37 standard test functions of dimensions ranging from 1 to 100 show that the SNMA has very good scalability and outperforms other algorithms in accurately locating multiple optima, both global and local. © 2011 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Genetic algorithms; Local learning; Memetic algorithms; Multimodal function optimization; Multiple optimal determination; Niching
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DOCUMENT TYPE: Article
SOURCE: Scopus
Bayram, S., Gezici, S.
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
(2012) Digital Signal Processing: A Review Journal, 22 (3), pp. 391-406.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84858706485&partnerID=40&md5=8b25b5c63fd115a6415000b1027eb446
AFFILIATIONS: Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey
ABSTRACT: Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman-Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results. © 2012 Elsevier Inc. All rights reserved.
AUTHOR KEYWORDS: Binary hypothesis-testing; Composite hypothesis-testing; Least-favorable prior; Neyman-Pearson; Stochastic resonance (SR)
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DOCUMENT TYPE: Article
SOURCE: Scopus
Gou, J., Wu, Z., Wang, J.
An improved particle swarm optimization algorithm based on self-adapted comprehensive learning
(2012) Advanced Science Letters, 11 (1), pp. 668-675.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864397180&partnerID=40&md5=a66a09160251bcc8b13bbe6c85d81dd6
AFFILIATIONS: College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
ABSTRACT: Particle swarm optimization (PSO) algorithm is often trapped in local optima and low accuracy in convergence. In this work, following the analysis of PSO's premature convergence, we propose a novel PSO algorithm called ACL-PSO based on self-adapted comprehensive learning. Population centroid learning mechanism is introduced into the algorithm. Compared to other four improved PSO algorithms in terms of accuracy, convergence speed and computational complexity, ACL-PSO converges faster, resulting in more robust and better optima. © 2012 American Scientific Publishers. All rights reserved.
AUTHOR KEYWORDS: Comprehensive Learning; Population Centroid; PSO
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DOCUMENT TYPE: Article
SOURCE: Scopus
Morsly, Y.a b , Aouf, N.b , Djouadi, M.S.a , Richardson, M.b
Particle swarm optimization inspired probability algorithm for optimal camera network placement
(2012) IEEE Sensors Journal, 12 (5), art. no. 6043848, pp. 1402-1412.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859887593&partnerID=40&md5=4b470419576a10db3b1f76415e5cde85
AFFILIATIONS: Robotics Laboratory, Military Polytechnics Institute, Algeirs 16111, Algeria;
Department of Informatics and Systems Engineering, Cranfield University, Shrivenham SN6 8LA, United Kingdom
ABSTRACT: In this paper, a novel method based on binary Particle Swarm Optimization (BPSO) inspired probability is proposed to solve the camera network placement problem. Ensuring accurate visual coverage of the monitoring space with a minimum number of cameras is sought. The visual coverage is defined by realistic and consistent assumptions taking into account camera characteristics. In total, nine evolutionary-like algorithms based on BPSO, Simulated Annealing (SA), Tabu Search (TS) and genetic techniques are adapted to solve this visual coverage based camera network placement problem. All these techniques are introduced in a new and effective framework dealing with constrained optimizations. The performance of BPSO inspired probability technique is compared with the performances of the stochastic variants (e.g., genetic algorithms-based or immune systems-based) of optimization based particle swarm algorithms. Simulation results for 2-D and 3-D scenarios show the efficiency of the proposed technique. Indeed, for a large-scale dimension case, BPSO inspired probability gives better results than the ones obtained by adapting all other variants of BPSO, SA, TS, and genetic techniques. © 2012 IEEE.
AUTHOR KEYWORDS: Camera network placement; discrete particle swarm optimization; evolutionary-like algorithms; immune system; sensor coverage; sensor networks
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wei, R., Ying, X., Xia, H.
Study on warship combat system design using DMOPSO algorithm
(2012) Advanced Materials Research, 482-484, pp. 1963-1968.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859204561&partnerID=40&md5=1d4163dd6fb8ae61f4d2d9225b40602f
AFFILIATIONS: Department of Naval Architecture and Ocean Engineering, University of Naval Engineering, Wuhan, China
ABSTRACT: A new method for selecting warship combat system during the period of warship alternatives conceptual design was discussed in the paper. After computing the overall measure of performance (OMOE) and overall measure of risk (OMOR) of all combat sub-system using analytic hierarchy process and utility function, the design variables representing this equipment alternatives were selected using discrete particle swarm. Niche technique was used for constructing non-dominated sort set and TOPSIS method was adopted to sort the final pareto solution. The results of selecting equipment alternatives in warship combat system show that DMOPSO (discrete multi-objective particle swarm optimization) can search the overall pareto solution effectively. © (2012) Trans Tech Publications, Switzerland.
AUTHOR KEYWORDS: Discrete multi-objective PSO; Multi-objective Optimization; Warship combat system
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Wasimi, S.A., Hassa, S.
Social considerations in domestic water pricing: A case study of Perth, Western Australia
*
(2012) Australian Journal of Water Resources, 15 (2), pp. 131-144.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859041010&partnerID=40&md5=84413b8569ed367ae77b91118bff4312
AFFILIATIONS: CQUniversity, Rockhampton, QLD, Australia
ABSTRACT: Domestic water pricing is a challenging balancing act of the three critical dimensions of sustainability: economic, environmental and social objectives (OECD, 2010). The increasing block tariff approach to water pricing is growing in popularity throughout the world because, arguably, it is seen to best address all three dimensions. However, social equity considerations are often at odds with other criteria and needs special scrutiny, as affordability and equity aspects may not be properly addressed especially when income and household size are not accounted for. This paper looks at social considerations that are relevant for decision making in water pricing for the city of Perth, Western Australia and proposes a pricing scheme that would address the social issues satisfactorily. The optimisation model, particle swarm optimisation, which has been used in this study can also be applied when multiple objective functions that include other considerations such as economic efficiency and environmental sustainability are used. © Institution of Engineers Australia, 2012.
AUTHOR KEYWORDS: Demand-adjusted pricing; Fixed water charge; Increasing block tariff; Particle swarm optimisation; Perth; Social considerations; Water consumption; Water Corporation; Water pricing; Water usage pattern
REFERENCES: Arbués, F., Villanúa, I., Barberán, R., Household size and residential water demand: An empirical approach (2010) Australian Journal of Agricultural and Resource Economics, 54 (1), pp. 61-80;
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Bar-Shira, Z., Finkelshtain, I., Simhon, A., Block-rate versus uniform water pricing in agriculture: An empirical analysis (2006) American Journal of Agricultural Economics, 88 (4), pp. 986-999;
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Li, D.L., (2009) Constrained Multi-Objective Particle Swarm Optimization with Application in Power Generation, , PhD Thesis, CQUniversity, Australia;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Le Thi, H.A.a , Vaz, A.I.F.b , Vicente, L.N.c
Optimizing radial basis functions by d.c. programming and its use in direct search for global derivative-free optimization
(2012) TOP, 20 (1), pp. 190-214.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859508506&partnerID=40&md5=b3909bf1a87275c488abd98d47eb7481
AFFILIATIONS: Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), Paul Verlaine University, Metz, Ile du Saulcy, 57045 Metz, France;
Department of Production and Systems, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;
CMUC, Department of Mathematics, University of Coimbra, 3001-454 Coimbra, Portugal
ABSTRACT: In this paper, we address the global optimization of functions subject to bound and linear constraints without using derivatives of the objective function. We investigate the use of derivative-free models based on radial basis functions (RBFs) in the search step of direct-search methods of directional type. We also study the application of algorithms based on difference of convex (d. c.) functions programming to solve the resulting subproblems which consist of the minimization of the RBF models subject to simple bounds on the variables. Extensive numerical results are reported with a test set of bound and linearly constrained problems. © 2011 Sociedad de Estadística e Investigación Operativa.
AUTHOR KEYWORDS: d.c. programming; DCA; Derivative-free optimization; Direct-search methods; Global optimization; Radial basis functions; Search step
REFERENCES: Ali, M.M., Khompatraporn, C., Zabinsky, Z.B., A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems (2005) J Glob Optim, 31, pp. 635-672;
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Kiseleva, E., Stepanchuk, T., On the efficiency of a global non-differentiable optimization algorithm based on the method of optimal set partitioning (2003) J Glob Optim, 25, pp. 209-235;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Xiao, Z., Chen, W., Li, L.
An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation
(2012) Applied Mathematical Modelling, 36 (4), pp. 1444-1454.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84855654699&partnerID=40&md5=7e56dd76707aec1eaf672c7a8a27cada
AFFILIATIONS: School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
ABSTRACT: Supplier selection problem, considered as a multi-criteria decision-making (MCDM) problem, is one of the most important issues for firms. Lots of literatures about it have been emitted since 1960s. However, research on supplier selection under operational risks is limited. What's more, the criteria used by most of them are independent, which usually does not correspond with the real world. Although the analytic network process (ANP) has been proposed to deal with the problems above, several problems make the method impractical. This study first integrates the fuzzy cognitive map (FCM) and fuzzy soft set model for solving the supplier selection problem. This method not only considers the dependent and feedback effect among criteria, but also considers the uncertainties on decision making process. Finally, a case study of supplier selection considering risk factors is given to demonstrate the proposed method's effectiveness. © 2011 Elsevier Inc.
AUTHOR KEYWORDS: Fuzzy cognitive map; Fuzzy soft set; Particle swarm optimization; Risk evaluation; Supplier selection
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Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.S., Groumpos, P.P., Vrahatis, M.N., Fuzzy cognitive maps learning using particle swarm optimization (2005) J. Intell. Inform. Syst., 25, pp. 95-121;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wang, Y.a , Qian, J.b
Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization
(2012) Wireless Networks, 18 (3), pp. 307-318.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861809506&partnerID=40&md5=020ccc502ae8ea2d0dcf4ab0e81d9c8f
AFFILIATIONS: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;
School of Information Science and Engineering, Ningbo University, Ningbo, China
ABSTRACT: The management of the uncertainties over data is an urgent problem of novel applications such as cyberphysical system, sensor network and RFID data management. In order to adapt the characteristics of evolving over time of sensor data in real-time location tracing service based on RFID, a measuring algorithm for the Uncertainty of RFID Data-PPMU (a particle filter and particle swarm optimization-based measuring uncertainty algorithm for RFID Data) is proposed in this paper. PPMU can change the number of samples adaptively on the basis of K-L distance to adapt the evolution of RFID data, and PPMU introduces an improved PSO (particle swarm optimization) method to enhance the efficiency of re-sampling phase of SIRPF (sequential importance re-sampling particle filter). Meanwhile, PPMU defines a fitness function base on Conventional Weighted Aggregation for PSO that balances the importance between the priori density and likelihood density to detect the most optimal samples among candidate sample sets. It provides a measurement with confidence factor for initial tuples in the probability RFID database. Experiments on real dataset show the proposed method can effectively measure the underlying uncertainty over RFID data. Compared with existing algorithms, PPMU can be further improved particle degradation and particle impoverishment problem.
AUTHOR KEYWORDS: Adaptive; Cyber-physical system; Particle filter; Real-time location tracing service; RFID data; Uncertainty
REFERENCES: Derakhshan, R., Orlowska, M.E., Xue, L., RFID data management: Challenges and opportunities (2007) Proceeding of IEEE International conference on RFID, 2007., IEEE RFID 2007, 26-28 March, Texas, USA;
Benjelloun, O., Sarma, A., Halevy, A., Widom, J., Uldbs: Databases with uncertainty and lineage (2006) Proceeding of the 32th International Conference on very large data base (VLDB'06), , 12-15 September, Seoul, Korea;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Huang, M.-L.a , Hung, Y.-H.a , Lee, W.-M.b , Li, R.K.b , Wang, T.-H.a
Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis
(2012) Journal of Medical Systems, 36 (2), pp. 407-414.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863192451&partnerID=40&md5=52585d6b6eecf1c881517b735a0fbeee
AFFILIATIONS: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Chungshan Road, Taiping, Taichung 411, Taiwan;
Department of Industrial Engineering and Management, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan
ABSTRACT: Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively. © Springer Science+Business Media, LLC 2010.
AUTHOR KEYWORDS: ANFIS; Breast cancer; Case-based reasoning; Particle swarm optimizer
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Xiong, X., Kim, Y., Baek, Y., Rhee, D.W., Kim, S.-H., Analysis of breast cancer using data mining & statistical techniques (2005) Proceedings - Sixth Int. Conf. on Softw. Eng., Artificial Intelligence, Netw. and Parallel/Distributed Computing and First ACIS Int. Workshop on Self-Assembling Wireless Netw., SNPD/SAWN 2005, 2005, pp. 82-87., DOI 10.1109/SNPD-SAWN.2005.19, 1434871, Proceedings - Sixth Int. Conf. on Softw. Eng., Artif. Intelligence, Networking and Parallel/Distributed Computing and First ACIS Int. Workshop on Self-Assembling Wireless Networks, SNPD/SAWN 2005;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Jiang, Y.a , Hao, Z.a b , Yuan, G.a , Yang, Z.a
Multilevel thresholding for image segmentation through Bayesian particle swarm optimisation
(2012) International Journal of Modelling, Identification and Control, 15 (4), pp. 267-276.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859546446&partnerID=40&md5=d43be59510c5341e0dabd96cffc8d1e0
AFFILIATIONS: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong, China;
Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
ABSTRACT: A simpler and efficient PSO algorithm based on Bayesian theorem and the characters of intensity images is proposed, called as Bayesian particle swarm optimisation algorithm (BPSO). In BPSO, a new method is designed to assign the constriction coefficient of the 'social influence' term for each particle automatically and separately based on Bayesian theorem, so that they can have different levels of exploration and exploitation capabilities. A new population initialisation strategy is adopted to make the search more efficient according to the characters of multilevel thresholding in an image arranged from a low grey level to a high one. The experimental results indicate that BPSO can produce effective, efficient and smoother segmentation results in comparison with three existing methods on Berkeley datasets. Copyright © 2012 Inderscience Enterprises Ltd.
AUTHOR KEYWORDS: Bayesian theorem; Image segmentation; Multilevel thresholding; Particle swarm optimisation; PSO
REFERENCES: Abido, M.A., Optimal design of power-system stabilizers using particle swarm optimization (2002) IEEE Transactions on Energy Conversion, 17 (3), pp. 406-413., DOI 10.1109/TEC.2002.801992, PII 1011092002801992;
Chander, A., Chatterjee, A., Siarry, P., A new social and momentum component adaptive PSO algorithm for image segmentation (2011) Expert Systems with Applications, 38 (2), pp. 4998-5004;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Chen, W.-H.a , Chen, J.-H.b , Shao, S.-C.a
Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units
(2012) International Journal of Control, Automation and Systems, 10 (2), pp. 407-414.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84862023115&partnerID=40&md5=7de23942c8bb589169740e903c34eca8
AFFILIATIONS: Institute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan;
Department of Communication Engineering, Oriental Institute of Technology, New Taipei, Taiwan
ABSTRACT: Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interference or lack of calibration. In this study, an effective approach based on the fusion of the general regression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.© ICROS, KIEE and Springer 2012.
AUTHOR KEYWORDS: General regression neural networks; Particle swarm optimization; Remote terminal units; Supervisory control and data acquisition (SCADA) systems
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Kumar, P., Chandna, V.K., Thomas, M.S., Fuzzy-genetic algorithm for preprocessing data at the RTU (2004) IEEE Trans. Power Systems, 19 (2), pp. 718-723., May;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Vaščák, J.
Adaptation of fuzzy cognitive maps by migration algorithms
(2012) Kybernetes, 41 (3), pp. 429-443.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861552524&partnerID=40&md5=47d07448cca8727694525113178f1e0c
AFFILIATIONS: Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Slovakia
ABSTRACT: Purpose: Conventional rule-based systems are insufficient for description of complex dynamic systems requiring nontrivial decision procedures. Fuzzy cognitive maps seem to be convenient to overcome these limitations. However, they lack ability of self-learning and therefore some adaptation approaches are needed. The purpose of this paper is both to show the use of fuzzy cognitive maps for such systems and to present migration algorithms as convenient adaptation means. Design/methodology/approach: Some problems of a complex dynamic system description by knowledge-based means are discussed. Fuzzy cognitive maps are presented as a possible way to solve these problems followed by description of migration algorithms as their adaptation means. Their use is clarified on an example of the so-called parking problem based on path planning using a graph search algorithm and a traffic simulation system. Findings: After series of simulations the reality of the proposed system and selected methods with their modifications was proved. It has shown the robustness of the presented solution under circumstances of uncertainty, too. Research limitations/implications: The paper points to stability investigation of the proposed approach introducing uncertainties into the traffic simulation system to take into account, e.g. unexpected events. Further, a possibility of developing a linguistic information retrieval system is mentioned. Practical implications: The proposed approach can find various implementations not only in planning tasks but also in robotic navigation and multi-agent applications in general. In addition, it suggests possibilities of knowledge-based systems, directly using human-like approaches, to areas of decision making under uncertainties and contradictories. Originality/value: An new modification of migration algorithms for adaptation of parameters for fuzzy cognitive maps is introduced and compared to other known self-learning methods. Further, the concept of a traffic simulation system for path planning is presented. © Emerald Group Publishing Limited.
AUTHOR KEYWORDS: Adaptation methods; Fuzzy cognitive maps; Migration algorithms; Path planning; Self managed learning; Simulation
REFERENCES: Aguilar, J., A dynamic fuzzy-cognitive-map approach based on random neural networks (2003) International Journal of Computational Cognition, 1 (4), pp. 91-107;
Blanco, A., Delgado, M., Pegalajar, M.C., Fuzzy automaton induction using neural network (2001) International Journal of Approximate Reasoning, 27 (1), pp. 1-26;
Blažič, S., Škrjanc, I., Design and stability analysis of fuzzy model-based predictive control - a case study (2007) Journal of Intelligent and Robotic Systems, 49 (3), pp. 279-292;
Chen, S.M., Cognitive-map-based decision analysis based on NPN logics (1995) Fuzzy Sets and Systems, 71 (2), pp. 155-163;
Coelho, L.S., Self-organizing migration algorithm applied to machining allocation of clutch assembly (2009) Mathematics and Computers in Simulation, 80 (2), pp. 427-435;
Groumpos, P.P., Fuzzy cognitive maps: Basic theories and their application to complex systems (2010) Fuzzy Cognitive Maps, pp. 1-23., Glykas, M. (Ed.), Springer, Berlin;
Johanyák, Z.C., Kovács, S., A brief survey and comparison on various interpolation-based fuzzy reasoning methods (2006) Acta Polytechnica Hungarica, 3 (1), pp. 91-105;
Kosko, B., Fuzzy cognitive maps (1986) International Journal of Man-Machine Studies, 24 (1), pp. 65-75;
LaValle, S.M., (2006) Planning Algorithms, , Cambridge University Press;
Martinez, L., Ruan, D., Herrera, F., Computing with words in decision support systems: An overview on models and applications (2010) International Journal of Computational Intelligence Systems, 3 (4), pp. 382-395;
Nollea, L., Zelinka, I., Hopgood, A.A., Goodyear, A., Comparison of an self-organizing migration algorithm with simulated annealing and differential evolution for automated waveform tuning (2005) Advances in Engineering Software, 36 (10), pp. 645-653;
Ortiz-Arroyo, D., Christensen, H.U., An optimized soft computing-based passage retrieval system (2009) Control and Cybernetics, 38 (2), pp. 455-479;
Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P., Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links (2006) International Journal of Human-Computer Studies, 64 (8), pp. 727-743;
Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.D., Groumpos, P.P., Vrahatis, M.N., Fuzzy cognitive maps learning using particle swarm optimization (2005) International Journal of Intelligent Information Systems, 25 (1), pp. 95-121;
Parsopoulos, K.E., Papageorgiou, E.I., Groumpos, P.P., Vrahatis, M.N., Evolutionary computation techniques for optimizing fuzzy cognitive maps in radiation therapy systems (2004) Lecture Notes in Computer Science, 3102, pp. 402-413., Deb, K. (Ed.), Springer, Berlin;
Preitl, S., Precup, R.E., Fodor, J., Bede, B., Feedback tuning in fuzzy control systems (2006) Theory and Applications. Acta Polytechnica Hungarica, 3 (3), pp. 81-96;
Shen, F., Yu, H., Kamiya, Y., Hasegawa, O., An online incremental semi-supervised learning method (2010) Journal of Advanced Computational Intelligence and Intelligent Informatics, 14 (6), pp. 593-605;
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M., Genetic learning of fuzzy cognitive maps (2005) Fuzzy Sets and Systems, 153 (2), pp. 371-401;
Vaščák, J., Evolutionary migration algorithms for scheduling (2005) Proceedings of the 3rd International Symposium on Applied Machine Intelligence and Informatics, Budapest Polytechnic, Budapest, Hungary, pp. 21-32;
Vaščák, J., Madarász, L., Adaptation of fuzzy cognitive maps - a comparison study (2010) Acta Polytechnica Hungarica, 7 (3), pp. 109-122;
Zelinka, I., (2002) Artificial Intelligence in Problems of Global Optimization, , (in Czech), BEN, Prague
DOCUMENT TYPE: Article
SOURCE: Scopus
Wang, H.
Particle Swarm Optimization with hybrid jumps for multimodal function optimization
(2012) Journal of Information and Computational Science, 9 (4), pp. 1115-1124.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859741884&partnerID=40&md5=99c669f28ed32b7ffb8720cc41cb1a16
AFFILIATIONS: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
ABSTRACT: Particle Swarm Optimization (PSO) has shown good performance in many optimization problems. However, it easily falls into local optima and suffers from premature convergence on complex multimodal problems. To help trapped particles escape from local minima, a novel hybrid jumps strategy is proposed. The main idea of the new jump strategy is to monitor the changes of previous best particle and the global best particle. If the best particles are trapped into local optima, hybrid jumps will be conducted to help the particles escape. Experimental studies on six multimodal benchmark problems show that PSO with Hybrid Jumps (PSOHJ) outperforms PSO, PSO with Gaussian Jump (PSOGJ), PSO with Cauchy Jump (PSOCJ), Classical Evolutionary Programming (CEP) and PSO with Gaussian Mutation (PSO-GM). Additionally, the number of successful jumps in different stages of evolution is also investigated. Copyright © 2012 Binary Information Press.
AUTHOR KEYWORDS: Cauchy jump; Gaussian jump; Multimodal function optimization; Particle swarm optimization (PSO)
REFERENCES: Kennedy, J., Eberhart, R.C., Particle swarm optimization (1995) Proc. of IEEE Int. Conf. Neural Networks, pp. 1942-1948;
Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N., Objective function "stretching" to alleviate convergence to local minima (2001) Nonlinear Analysis TMA, 47, pp. 3419-3424;
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Shi, Y., Eberhart, R.C., A modified particle swarm optimizer (1998) Proceedings of IEEE Congress on Evolutionary Computation, pp. 69-73;
Wang, H., Liu, Y., Li, C.H., Zeng, S.Y., A hybrid particle swarm algorithm with Cauchy mutation (2007) IEEE Swarm Intelligence Symposium, pp. 356-360., Honolulu, Hawaii;
Wang, H., Liu, Y., Zeng, S.Y., Li, H., Li, C.H., Opposition-based particle swarm algorithm with Cauchy mutation (2007) Proceedings of IEEE Congress on Evolutionary Computation, pp. 4750-4756;
Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., Ventresca, M., Enhancing particle swarm optimization using generalized opposition-based learning (2011) Information Sciences, 181 (20), pp. 4699-4714;
Wang, H., Theoretical analysis of adaptive Cauchy mutation in particle swarm optimization (2012) Journal of Computational Information Systems, , in press;
Wang, H., Wu, Z., Zeng, S., Jiang, D., Liu, Y., Wang, J., Yang, X., A simple and fast particle swarm optimization (2010) Journal of Multiple-Valued Logic and Soft Computing, 16 (6), pp. 611-629;
Pan, X., Cao, Y., Zhang, H., Opposition-based particle swarm optimizer with passive congregation (2011) Journal of Computational Information Systems, 7 (14), pp. 5024-5031
DOCUMENT TYPE: Article
SOURCE: Scopus
Sahoo, N.C.a , Ganguly, S.b , Das, D.b
Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization
(2012) Swarm and Evolutionary Computation, 3, pp. 15-32.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857056396&partnerID=40&md5=f20506906baf850f731e8e405f3bafc8
AFFILIATIONS: Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia;
Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, India
ABSTRACT: A multi-objective planning approach for electrical distribution systems using particle swarm optimization is presented in this paper. In this planning, the number of feeders and their routes, number and locations of sectionalizing switches, and number and locations of tie-lines of a distribution system are optimized. The multiple objectives to determine optimal values for these planning variables are: (i) minimization of total installation and operational cost and (ii) maximization of network reliability. The planning optimization is performed in two steps. In the first step, the distribution network structure, i.e., number of feeders, their routes, and number and locations of sectionalizing switches are determined. In the second step, the optimum number and locations of tie-lines are determined. Both the objectives are minimized simultaneously to obtain a set of non-dominated solutions in the first step of optimization. The solution strategy used for the first step optimization is the Strength Pareto Evolutionary Algorithm-2 (SPEA2) based multi-objective particle swarm optimization (SPEA2MOPSO). In the second step, the solutions/networks obtained from the previous step are further optimized by placement of tie-lines. SPEA2-based binary MOPSO (SPEA2BMOPSO) is used in the second step of optimization. The proposed planning algorithm is tested and evaluated on different practical distribution systems. © 2011 Elsevier B.V. All rights reserved.
AUTHOR KEYWORDS: Cost-biased encoding; Multi-objective optimization; Pareto-dominance; Particle swarm optimization; Power distribution system planning
REFERENCES: Gonen, T., Ramirez-Rosado, I.J., Review of distribution system planning models: A model for optimal multistage planning (1986) IEE Proceedings C: Generation Transmission and Distribution, 133 (7), pp. 397-408;
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Bhowmik, S., Goswami, S.K., Bhattacherjee, P.K., A new power distribution system planning through reliability evaluation technique (2000) Electric Power System Research, 54, pp. 169-179;
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Nahman, J., Spiric, J., Optimal planning of rural medium voltage distribution networks (1997) Electrical Power and Energy System, 19 (8), pp. 549-556;
Fletcher, R.H., Strunz, K., Optimal distribution system horizon planning - Part I: Formulation (2007) IEEE Transactions on Power Systems, 22 (2), pp. 791-799., DOI 10.1109/TPWRS.2007.895173;
Fletcher, R.H., Strunz, K., Optimal distribution system horizon planning - Part II: Application (2007) IEEE Transactions on Power Systems, 22 (2), pp. 862-870., DOI 10.1109/TPWRS.2007.895177;
Boulaxis, N.G., Papadopoulos, M.P., Optimal feeder routing in distribution system planning using dynamic programming technique and GIS facilities (2002) IEEE Transactions on Power Delivery, 17 (1), pp. 242-247., DOI 10.1109/61.974213, PII S0885897702005435;
Miranda, V., Ranito, J.V., Proenca, L.M., Genetic algorithm in optimal multistage distribution network planning (1994) IEEE Transactions on Power System, 9 (4), pp. 1927-1931;
Tang, Y., Power distribution systems planning with reliability modeling and optimization (1995) IEEE Transactions on Power System, 11 (1), pp. 181-189;
Ramirez-Rosado, I.J., Bernal-Agustin, J.L., Reliability and costs optimization for distribution networks expansion using an evolutionary algorithm (2001) IEEE Transactions on Power Systems, 16 (1), pp. 111-118., DOI 10.1109/59.910788, PII S0885895001032059;
Carrano, E.G., Soares, L.E., Takahashi, R.C., Saldanha, R.R., Neto, O.M., Electric distribution network multiobjective design using a problem-specific genetic algorithm (2006) IEEE Transactions on Power Delivery, 21 (2), pp. 995-1005;
Mendoza, F., Bernal-Agustin, J.L., Dominguez-Navarro, J.A., NSGA and SPEA applied to multiobjective design of power distribution systems (2006) IEEE Transactions on Power Systems, 21 (4), pp. 1938-1945., DOI 10.1109/TPWRS.2006.882469;
Ramirez-Rosado, I.J., Dominguez-Navarra, J.A., Possibilistic model based on fuzzy sets for the multiobjective optimal planning of electric power distribution networks (2004) IEEE Transactions on Power System, 19 (4), pp. 1801-1810;
Carrano, E.G., Guimaraes, F.G., Takahashi, R.H.C., Neto, O.M., Campelo, F., Electric distribution network expansion under load-evolution uncertainty using an immun system inspired algorithm (2007) IEEE Transactions on Power Systems, 22 (2), pp. 851-861., DOI 10.1109/TPWRS.2007.894847;
Ganguly, S., Sahoo, N.C., Das, D., A novel multi-objective PSO for electrical distribution system planning incorporating distributed generation (2010) Journal of Energy Systems, 1 (3), pp. 291-337;
Ganguly, S., Sahoo, N.C., Das, D., Multi-objective planning of electrical distribution systems using particle swarm optimization Proceedings of International Conference on Electric Power and Energy Conversion Systems, IEEE, , Sharjah, UAE, 2009, available in IEEEXplore;
Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, , John Wiley and Sons Ltd: Reprinted copy October 2004;
Zitzler, E., Laumanns, M., Thiele, L., SPEA2: Improving the strength Pareto evolutionary algorithm (2001) Computer Engineering and Networks Laboratory Technical Report-103, , Zurich, Switzerland;
Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.-C., Harley, R.G., Particle swarm optimization: Basic concepts, variants and applications in power systems (2008) IEEE Transactions on Evolutionary Computation, 12 (2), pp. 171-195., DOI 10.1109/TEVC.2007.896686;
Sierra, M.R., Coello Coello, C.A., Multi-objective particle swarm optimizers: A survey of the state-of-the-art (2006) International Journal of Computational Intelligence and Research, 2 (3), pp. 287-308;
Parsopoulos, K.E., Vrahatis, M.N., Multiobjective particle swarm optimization approaches (2008) Multi-Objective Optimization in Computational Intelligence: Theory and Practice, pp. 20-42., (Chapter 2), IGI Global;
Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method in multiobjective problems (2002) Proceedings of the ACM Symposium on Applied Computing, pp. 603-607;
Hu, X., Eberhart, R., Multiobjective optimization using dynamic neighborhood particle swarm optimization (2002) Proceedings of the Congress on Evolutionary Computation, 2, pp. 1677-1681;
Hu, X., Eberhart, R., Shi, Y., Particle swarm with extended memory for multiobjective optimization (2003) Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, pp. 193-197;
Mostaghim, S., Teich, J., Strategies for finding good local guides in multi-objective particle swarm optimization MOPSO (2003) Proceedings of the IEEE Swarm Intelligence Symposium, pp. 26-33;
Mostaghim, S., Teich, J., The role of ε-dominance in multi objective particle swarm optimization methods (2003) Congress on Evolutionary Computation, 3, pp. 1764-1771;
Li, X., A nondominated sorting particle swarm optimizer for multiobjective optimization (2003) Lecture Notes in Computer Science, 2723, pp. 37-48;
Coello Coello, C.A., Pulido, G.T., Lechuga, M.S., Handling multiple objectives with particle swarm optimization (2004) IEEE Transactions on Evolutionary Computation, 8 (3), pp. 256-279;
Alvarez-Benitez, J.E., Everson, R.M., Fieldsend, J.E., A MOPSO algorithm based exclusively on pareto dominance concepts (2005) Lecture Notes in Computer Science, 3410, pp. 459-473., Evolutionary Multi-Criterion Optimization - Third International Conference, EMO 2005;
Zhang, Q., Xue, S., An improved multi-objective particle swarm optimization algorithm (2007) Lecture Notes in Computer Science, 4683, pp. 372-381;
Mostaghim, S., Branke, J., Schmeck, H., Multi-objective particle swarm optimization on computer grids (2007) Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, pp. 869-875., DOI 10.1145/1276958.1277127, Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference;
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Agrawal, S., Dashora, Y., Tiwari, M., Son, Y.-J., Interactive particle swarm: A Pareto-adaptive metaheuristic to multiobjective optimization (2008) IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 38 (2), pp. 258-277., DOI 10.1109/TSMCA.2007.914767;
Durillo, J.J., Garc'Ia-Nieto, J., Nebro, A.J., Coello Coello, C.A., Luna, F., Alba, E., Multi-objective particle swarm optimizers: An experimental comparison (2009) Lecture Notes in Computer Science, 5467, pp. 495-509;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Xia, K.-W., Cai, J., Liu, N.-P., Zhou, Y.-T.
Design on ultra wide band pulse waveform
(2012) Advances in Information Sciences and Service Sciences, 4 (6), pp. 93-101.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859742689&partnerID=40&md5=2940d63e11931e51c807fc468d51eca5
AFFILIATIONS: School of Information Engineering, Hebei University of Technology, Tianjin 300401, China
ABSTRACT: It is found that design on Pulse signal waveform plays an important role in the Ultra Wide Band (UWB) communication system. Since design on ordinary pulse waveform can not take into account the constrain conditions of UWB both in time domain and frequency domain, there is poor application in practice. Therefore, UWB pulse transmitting circuit is designed based on analysis of ordinary Gaussian pulse circuit. Firstly, ordinary Gaussian pulse circuit is improved by adding attenuation network and high-pass filter; then main parameters of pulse transmitting circuit are optimized by using particle swarm optimization (PSO) algorithm so that output waveform can meet standard of UWB waveform design. Simulation experiment and interference test show that various performance indexes of pulse waveform meet technical requirements of UWB system.
AUTHOR KEYWORDS: Circuit optimization; Gaussian pulse circuit; Particle swarm optimization algorithm; Pulse waveform design; Ultra wide band (UWB)
REFERENCES: Win, M.Z., Scholtz, R.A., On the energy capture of ultrawidebandwidth signals in dense multipath environments (2008) IEEE Communications Letters, 2 (9), pp. 245-247;
Zhang, H.G., Kohno, R., Soft-spectrum adaptation in UWB impulse radio (2003) Proc of IEEE 14th Conf On PIMR, pp. 289-293;
Parr, B., Cho, B.L., Wallace, K., Ding, Z., A novel ultra-wideband pulse design algorithm (2003) IEEE Communications Letters, 3 (5), pp. 219-221;
Win, M.Z., Scholtz, R.A., Ultra-Wide Bandwidth Time-Hopping Spread-Spectrum Impulse Radio for Wireless Multiple-Access Communications (2000) IEEE Trans On Communications, 48 (4), pp. 679-690;
Gaing, Z.L., A particle swarm optimization approach for optimum design of PID controller in AVR system (2004) IEEE Trans On Energy Conversion, 19 (2), pp. 384-391;
Li, L.L., Wang, L., Liu, L., An effective hybrid PSOSA strategy for optimization and its application to parameter estimation (2006) Applied Mathematics and Computation, 179 (1), pp. 135-146;
Liu, S., Blind, W.J., A daptive Multiuser Detection Using a Recurrent Neural Network[J] (2004) IEEE Trans Commun, 7 (4), pp. 1071-1705;
Robinson, J., Rahmat-Samii, Y., Particle swarm optimization in electromagnetics (2004) IEEE Transactions On Antennas and Propaga-tion, 52 (2), pp. 397-407;
Paulino, N., Goes, J., Design methodology for optimization of analog building blocks using genetic algorithms (2001) IEEE ISCAS, 11 (5), pp. 6-9;
Ren, W., Compact Microstrip-fed Monopole Antenna for UWB Applications (2011) AISS, 3 (8), pp. 146-153;
Li, L., Wang, L., Liu, L., An effective hybrid PSOSA strategy for optimization and its application to parameter estimation (2006) Applied Mathematics and Computation, 179 (1), pp. 135-146;
Liang, T., Zheng, Z., Lei, S., UWB Channel Estimation Based on Distributed Bayesian Compressive Sensing (2011) JDCTA, 5 (2), pp. 1-8;
Win, M.Z., Scholtz, R.A., Ultra-wideband width Time-Hopping Spread-Spectrum impulse radio for wireless multiple-access communications (2009) IEEE Trans On Communications, 48 (4), pp. 679-690;
Roy, S., Foerster, J.R., Srinivasa Somayazulu, V., Leeper, D.G., Ultra-wideband Radio Design: The Promise of High-speed, Short-range Wireless Connectivity (2009) Proceedings of the International Information Technology, 12 (2), pp. 295-311;
Parsopoulos, K.E., Vrahatis, M.N., (2002) Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization,Natural Computing, 11 (2), pp. 235-306;
Loen, J.D., Colombano, S.P., A circuit representation technique for automated circuit design (2009) IEEE Transactions On Evolutionary Computation, 33 (2), pp. 145-151;
Koza, J.R., Automated synthesis of analog electrical circuits by means of genetic programming (2006) IEEE Transactions On Evolutionary Computation, 20 (3), pp. 245-253
DOCUMENT TYPE: Article
SOURCE: Scopus
Chen, Z.a , Zhu, J.-H.b , Yu, L.b
An improved PSO algorithm for structure damage identification
(2012) Zhendong yu Chongji/Journal of Vibration and Shock, 31 (5), pp. 17-20.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84861715212&partnerID=40&md5=7f4f6c03733318ef2ee69bd102dbeb73
AFFILIATIONS: School of Civil Engineering and Communication, North China Institute of Water Conservancy and Hydroelectric Power, Zhengzhou 450011, China;
Department of Mechanics and Civil Engineering, Jinan University, Guangzhou 510632, China
ABSTRACT: Based on the characteristic that damages occurring in a structure are usually local, an improved particle swarm optimization (PSO) algorithm for structure damage detection was developed here by adding a zero mutation ratio coefficient (ZMRC) into PSO. The numerical simulation of a single damage for a two-story rigid frame showed that the proposed method has many advantages, such as, faster convergence of objective function, better identification accuracy, robust noise immunity and less effect of size of population, compared with PSO. Further, the effect of the weighted coefficients of objective function on identification accuracy was investigated. All these advantages were very meanful for application of the proposed method in complex structure damage detection.
AUTHOR KEYWORDS: Constrained optimization; Damage detection; Particle swarm optimization (PSO); Zero mutation ratio coefficient (ZMRC)
REFERENCES: Kennedy, J., Eberhart, R.C., Shi, Y., (2001) Swarm Intelligence, , San Francsco: Morgan Kaufmann Publishers;
Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method for constrained optimization problems (2002) Intelligent Technologies: From Theory to Applications, pp. 214-220., Amsterdam: IOS Press;
Shi, Y.H., Eberhart, R.C., A modified particle swarm optimizer (1998) IEEE World Congress on Computational Intelligence, pp. 69-73;
(2004), 17 (3), pp. 350-353., Chinese source(2006), 25 (5), pp. 37-39., Chinese source(2006), 23 (SUPPL.1), pp. 73-78., Chinese source(2011), 30 (5), pp. 174-178., Chinese source(2009), 28 (1), pp. 1-3., Chinese source(2009), 28 (11), pp. 183-187., Chinese source(2006), 19 (4), pp. 525-531., Chinese sourceDOCUMENT TYPE: Article
SOURCE: Scopus
García-Nieto, J.a , Alba, E.a , Carolina Olivera, A.b
Swarm intelligence for traffic light scheduling: Application to real urban areas
(2012) Engineering Applications of Artificial Intelligence, 25 (2), pp. 274-283.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84855806275&partnerID=40&md5=1705701a1eb6ca106efbcd1def879e3a
AFFILIATIONS: Dept. de Lenguajes y Ciencias de la Computación, University of Málaga, Campus de Teatinos, Málaga 29071, Spain;
Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional Del sur, Av. Alem 1253, 8000 Bahía Blanca, Argentina
ABSTRACT: Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Málaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time. © 2011 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Cycle program optimization; Particle swarm optimization; Realistic traffic instances; SUMO microscopic simulator of urban mobility; Traffic light scheduling
REFERENCES: Alba, E., García-Nieto, J., Jourdan, L., Talbi, E.-G., (2007) Gene Selection in Cancer Classification Using PSO/SVM and GA/SVM Hybrid Algorithms, pp. 284-290;
Alba, E., Luque, G., García-Nieto, J., Ordonez, G., Leguizamón, G., Mallba: A software library to design efficient optimisation algorithms (2007) Int. J. Innovative Comput. Appl. (IJICA), 1 (1), pp. 74-85;
Alba, E., García-Nieto, J., Taheri, J., Zomaya, A., New research in nature inspired algorithms for mobility management in GSM networks (2008) Lecture Notes in Computer Science of the Fifth European Workshop on the Application of Nature-inspired Techniques to Telecommunication Networks and Other Connected Systems, p. 110., EvoWorkshops08, Napoli, Italy;
Angulo, E., Romero, F.P., García, R., Serrano-Guerrero, J., Olivas, J.A., A methodology for the automatic regulation of intersections in real time using soft-computing techniques (2008) Modelling, Computation and Optimization in Information Systems and Management Sciences, pp. 379-388., Springer;
Blum, C., Roli, A., Metaheuristics in combinatorial optimization: Overview and conceptual comparison (2003) ACM Comput. Surveys (CSUR), 35 (3), pp. 268-308;
Brockfeld, E., Barlovic, R., Schadschneider, A., Schreckenberg, M., Optimizing traffic lights in a cellular automaton model for city traffic (2001) Phys. Rev. e, 64 (5), p. 056132;
Chen, J., Xu, L., Road-junction traffic signal timing optimization by an adaptive particle swarm algorithm (2006) ICARCV, p. 17;
Clerc, M., Kennedy, J., The particle swarm-explosion, stability, and convergence in a multidimensional complex space (2002) IEEE Transactions on Evolutionary Computation, 6 (1), pp. 58-73., DOI 10.1109/4235.985692, PII S1089778X02022099;
García-Nieto, J., Alba, E., Automatic parameter tuning with metaheuristics of the AODV routing protocol for vehicular ad-hoc networks (2010) Applications of Evolutionary Computation of Lecture Notes in Computer Science, 6025, pp. 21-30., Springer Berlin, Heidelberg;
García-Nieto, J., Toutouh, J., Alba, E., Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics (2010) Engineering Applications of Artificial Intelligence Advances in Metaheuristics for Hard Optimization New Trends and Case Studies, 23 (5), pp. 795-805;
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Krajzewicz, D., Brockfeld, E., Mikat, J., Ringel, J., Rössel, C., Tuchscheerer, W., Wagner, P., Wösler, R., (2005) Simulation of Modern Traffic Lights Control Systems Using the Open Source Traffic Simulation SUMO;
Krajzewicz, D., Bonert, M., Wagner, P., The open source traffic simulation package SUMO (2006) RoboCup 2006 Infrastructure Simulation Competition;
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Lim, G., Kang, J.J., Hong, Y., The optimization of traffic signal light using artificial intelligence (2001) FUZZ-IEEE, pp. 1279-1282;
McCrea, J., Moutari, S., A hybrid macroscopic-based model for traffic flow in road networks (2010) Eur. J. Oper. Res., 207 (1), pp. 676-684;
Montes De Oca, M.A., Stützle, T., Birattari, M., Dorigo, M., Frankenstein's PSO: A composite particle swarm optimization algorithm (2009) Trans. Evol. Comput., 13 (5), pp. 1120-1132;
Nagatani, T., Effect of speed fluctuation on green-light path in 2d traffic network controlled by signals (2010) Phys. A: Stat. Mech. Appl., 389 (19), pp. 4105-4115;
Parsopoulos, K.E., Vrahatis, M.N., Unified Particle Swarm Optimization for solving constrained engineering optimization problems (2005) Lecture Notes in Computer Science, 3612 (PART III), pp. 582-591., Advances in Natural Computation: First International Conference, ICNC 2005. Proceedings;
Peng, L., Hai Wang, M., Ping Du, J., Luo, G., Isolation niches particle swarm optimization applied to traffic lights controlling (2009) Proceedings of the 48th IEEE: Decision and Control Chinese Conference CDC/CCC, pp. 3318-3322;
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Sánchez Medina, J., Galán, M., Rubio Royo, E., Stochastic vs deterministic traffic simulator. comparative study for its use within a traffic light cycles optimization architecture (2005) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach of Lecture Notes in Computer Science, 3562, pp. 622-631., Springer Berlin, Heidelberg;
Sanchez, J., Galan, M., Rubio, E., Applying a traffic lights evolutionary optimization technique to a real case: "Las Ramblas" area in Santa Cruz de Tenerife (2008) IEEE Transactions on Evolutionary Computation, 12 (1), pp. 25-40., DOI 10.1109/TEVC.2007.892765;
Sheskin, D.J., (2007) Handbook of Parametric and Nonparametric Statistical Procedures, , Chapman & Hall/CRC;
Spall, J.C., Chin, D.C., Traffic-responsive signal timing for system-wide traffic control (1997) Transportation Research Part C: Emerging Technologies, 5 (3-4), pp. 153-163., PII S0968090X97000120;
Teklu, F., Sumalee, A., Watling, D., A genetic algorithm approach for optimizing traffic control signals considering routing (2007) Computer-Aided Civil and Infrastructure Engineering, 22 (1), pp. 31-43., DOI 10.1111/j.1467-8667.2006.00468.x;
Thain, D., Tannenbaum, T., Livny, M., Distributed computing in practice: The Condor experience (2005) Concurrency Computation Practice and Experience, 17 (2-4), pp. 323-356., DOI 10.1002/cpe.938, Grid Performance and Grids and Web Services for E-Science;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wang, H.Q.a b , Wang, P.B.a b , Wang, S.Z.a b , Li, M.L.a b
Study of intelligent optimization methods applied in the fractional Fourier transform
(2012) Advanced Materials Research, 461, pp. 323-328.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863155935&partnerID=40&md5=5af8354058935fe91078ffee1d8fdd72
AFFILIATIONS: Navy University of Engineering, Wuhan, China;
PLA NO. 91329 Troop, Weihai, China
ABSTRACT: In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, some typical intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm, particle swarm optimization and chaos optimization method are introduced and applied successfully in fractional Fourier transform. The performances of the global optimization methods are compared with step-based method based on simulation. Results show that the COA optimization algorithm is much more preferable considering computation efficiency, precision and resolution in all the above mentioned optimization methods. © (2012) Trans Tech Publications, Switzerland.
AUTHOR KEYWORDS: Chaos optimization algorithm; Continuous ant colony algorithm; Extremum seeking; Genetic algorithms; Particle swarm optimization; The fractional Fourier transform
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DOCUMENT TYPE: Conference Paper
SOURCE: Scopus
Zhu, Y., Yang, J.-Y., Gao, L.-J., Chen, X.-L.
Research on optimal reactive power planning for distribution network containing asynchronous wind power generators
(2012) Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 40 (5), pp. 80-84+132.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863338999&partnerID=40&md5=7549d5be03fd4b4d896a38f3aa05d8d2
AFFILIATIONS: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
ABSTRACT: The optimal reactive power planning for distribution network containing asynchronous wind power generators has been researched. A new comprehensive optimal model that is composed of cost-benefit ratio and static voltage stability index has been built based on adopting scenario analysis, and bus voltage constraints in various scenario are also introduced, which adapts to the characteristic of randomicity and fluctuation of wind power. Under the condition that the installed capacity and position of capacitors are unknown, an optimal planning scheme is attained by using improved PSO algorithm to solve the solution of constrained objective function. Through the case analysis of IEEE33 buses, the method has been proved to be correct and effective.
AUTHOR KEYWORDS: Asynchronous wind power generators; Cost-benefit; PSO algorithm; Reactive power planning
REFERENCES: Lin, G.-M., Ouyang, S., Zeng, J., Application of improved catastrophic genetic algorithms in optimal reactive power planning (2010) Power System Technology, 34 (4), pp. 128-132;
Feng, X.-K., Tai, N.-L., Song, K., Comparative analysis on the impact of the wind generator connected to the distribution network (2009) Power System Protection and Control, 37 (21), pp. 25-30;
Chen, H.-Y., Chen, J.-F., Duan, X.-Z., Reactive power optimization in distribution system with wind power generators (2008) Proceedings of the CSEE, 28 (7), pp. 40-45;
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He, Y.-Q., Peng, J.-C., Wen, M., Scenario model and algorithm for the reconfiguration of distribution network with wind power generators (2010) Proceedings of the CSEE, 30 (28), pp. 12-30;
Zhao, L., Lü, J.-H., Multi-objective reactive power optimization of wind farm based on improved genetic algorithm (2010) Electric Power Automation Equipment, 30 (10), pp. 84-88;
Abu-Mouti, F.S., El-Hawary, M.E., A new and fast power flow solution algorithm for radial distribution feeders including distributed generations (2007) Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2668-2673;
Wu, X.-M., Liu, J., Bi, P.-X., Research on voltage stability of distribution networks (2006) Power System Technology, 30 (24), pp. 31-35;
Parsopoulos, K.E., Vrahatis, M.N., Particle swarm optimization method for constrained optimization problems (2002) Intelligent Technologies-Theory and Applications;
Shi, L.-J., Tang, G.-Q., FESS PI parameter optimization by an improved PSO algorithm (2010) Power System Protection and Control, 38 (10), pp. 53-55;
Baran, M.E., Wu, F.F., Vrahatis, Network reconfiguration in distribution systems for loss reduction and load balancing (1989) IEEE Transactions on Power Delivery, 4 (2), pp. 1401-1407;
Yu, J.-M., Zhang, D., Yao, L.-X., Reactive power optimization of distribution network based on a new location algorithm for nodes to be compensated (2004) Power System Technology, 28 (1), pp. 67-70;
Feijao, A.E., Cidras, J., Modeling of wind farm in the load flow analysis (2000) IEEE Transactions on Power Systems, 15 (1), pp. 110-115
DOCUMENT TYPE: Article
SOURCE: Scopus
Hooshmand, R.-A., Hemmati, R., Parastegari, M.
Combination of AC transmission expansion planning and reactive power planning in the restructured power system
(2012) Energy Conversion and Management, 55, pp. 26-35.
http://www.scopus.com/inward/record.url?eid=2-s2.0-81355138475&partnerID=40&md5=b4e40a481fa7bbdd762d12794491025d
AFFILIATIONS: Department of Electrical Engineering, University of Isfahan, P.O. Box: 81746-73441, Isfahan, Iran
ABSTRACT: Transmission Expansion Planning (TEP) is an important issue in power system studies. It involves decisions on location and number of new transmission lines. Before deregulation of the power system, the goal of TEP problem was investment cost minimization. But in the restructured power system, nodal prices, congestion management, congestion surplus and so on, have been considered too. In this paper, an AC model of TEP problem (AC-TEP) associated with Reactive Power Planning (RPP) is presented. The goals of the proposed planning problem are to minimize investment cost and maximize social benefit at the same time. In the proposed planning problem, in order to improve the reliability of the system the Expected Energy Not Supplied (EENS) index of the system is limited by a constraint. For this purpose, Monte Carlo simulation method is used to determine the EENS. Particle Swarm Optimization (PSO) method is used to solve the proposed planning problem which is a nonlinear mixed integer optimization problem. Simulation results on Garver and RTS systems verify the effectiveness of the proposed planning problem for reduction of the total investment cost, EENS index and also increasing social welfare of the system. © 2011 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Nodal price; Particle Swarm Optimization; Reactive Power Planning; Reliability assessment; Social benefit; Transmission Expansion Planning
REFERENCES: Romero, R., Rocha, C., Mantovani, J.R.S., Sanchez, I.G., Constructive heuristic algorithm for the DC model in network transmission expansion planning (2005) IEE Proc Generat, Trans Distribut, 152, pp. 277-282;
Rider, M.J., Garcia, A.V., Romero, R., Power system transmission network expansion planning using AC model (2007) IET Generat, Trans Distribut, 1, pp. 731-742;
Rahmani, M., Rashidinejad, M., Carreno, E.M., Romero, R., Efficient method for AC transmission network expansion planning (2010) Electric Power Syst Res, 80, pp. 1056-1064;
Qu, G., Cheng, H., Yao, L., Ma, Z., Zhu, Z., Transmission surplus capacity based power transmission expansion planning (2010) Electric Power Syst Res, 80, pp. 19-27;
Leite Da Silva, A.M., Rezende, L.S., Da Fonseca Manso, L.A., De Resende, L.C., Reliability worth applied to transmission expansion planning based on ant colony system (2010) Electr Power Energy Syst, 32, pp. 1077-1084;
Georgilakis, P.S., Market-based transmission expansion planning by improved differential evolution (2010) Electr Power Energy Syst, 32, pp. 450-456;
Hyung Roh, J., Shahidehpour, M., Wu, L., Market-based generation and transmission planning with uncertainties (2009) IEEE Trans Power Syst, 24, pp. 1587-1598;
De La Torre, S., Conejo, A.J., Contreras, J., Transmission expansion planning in electricity markets (2008) IEEE Trans Power Syst, 23, pp. 238-248;
Garcés, L.P., Conejo, A.J., Bertrand, R.G., Romero, R., A bi-level approach to transmission expansion planning within a market environment (2009) IEEE Trans Power Syst, 24, pp. 1513-1522;
Al-Hamouz, Z.M., Al-Faraj, A.S., Transmission expansion planning based on a non-linear programming algorithm (2003) Appl Energy, 76, pp. 169-177;
Alguacil, N., Motto, A.L., Conejo, A.J., Transmission expansion planning: A mixed-integer LP approach (2003) IEEE Trans Power Syst, 18, pp. 1070-1077;
Akbari, T., Rahimikian, A., Kazemi, A., A multi-stage stochastic transmission expansion planning method (2011) Energy Convers Manage, 52, pp. 2844-2853;
Shayeghi, H., Mahdavi, M., Bagheri, A., Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem (2010) Energy Convers Manage, 51, pp. 112-121;
Da Silva, E.L., Ortiz, J.M.A., De Oliveira, G.C., Binato, S., Transmission network expansion planning under a Tabu search approach (2011) IEEE Trans Power Syst, 16, pp. 62-68;
Verma, A., Panigrahi, B.K., Bijwe, P.R., Harmony search algorithm for transmission network expansion planning (2010) IET Generat, Trans Distribut, 4, pp. 663-673;
Jalilzadeh, S., Kazemi, A., Shayeghi, H., Madavi, M., Technical and economic evaluation of voltage level in transmission network expansion planning using GA (2008) Energy Convers Manage, 19, pp. 1119-1125;
Mahdavi, M., Shayeghi, H., Kazemi, A., DCGA based evaluating role of bundle lines in TNEP considering expansion of substations from voltage level point of view (2009) Energy Convers Manage, 50, pp. 2067-2073;
Shayeghi, H., Jalilzadeh, S., Mahdavi, M., Hadadian, H., Studying influence of two effective parameters on network losses in transmission expansion planning using DCGA (2008) Energy Convers Manage, 49, pp. 3017-3024;
Haddadian, H., Hosseini, S.H., Shayeghi, H., Shayanfar, H.A., Determination of optimum generation level in DTEP using a GA-based quadratic programming (2011) Energy Convers Manage, 52, pp. 382-390;
Kirschen, D., Strbac, G., (2010) Fundamentals of Power System Economics, , John Wiley & Sons, Inc. England [chapter 6, page154];
Mithulananthan, N., Acharya, N., A proposal for investment recovery of FACTS devices in deregulated electricity markets (2007) Electric Power Syst Res, 77, pp. 695-703;
Milano, F., (2010) Power System Analysis Toolbox, Version 2.1.6;
Parsopoulos, K.E., Vrahatis, M.N., (2010) Particle Swarm Optimization and Intelligence: Advances and Applications, , Information Science Reference New York;
Kennedy, J., Eberhart, R.C., A discrete binary version of the particle swarm algorithm (1997) IEEE Int Conf Comput Cybernet Simulat, 5, pp. 12-15
DOCUMENT TYPE: Article
SOURCE: Scopus
VašČák, J., Pal'a, M.
Adaptation of fuzzy cognitive maps for navigation purposes by Migration Algorithms
(2012) International Journal of Artificial Intelligence, 8 (12 S), pp. 19-37.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863600015&partnerID=40&md5=1f591c364018afd6cf95bf465a63cf87
AFFILIATIONS: Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia
ABSTRACT: Fuzzy Cognitive Maps (FCM) represent not only a user-friendly knowledge representation but also a convenient means for simulation of dynamic systems and decision-making support. Concerning the nature of robotic systems FCM seem to be convenient in using mainly on upper decision levels. However, FCM strike on problems of their design. Beside manual approach, which is limited by the number of nodes and their connections, various adaptation methods have been proposed. This paper gives a short summary of these methods dividing them into Hebbian-based and evolutionary-based approaches. Further, it presents a new adaptation of the so-called Self-Organizing Migration Algorithms (SOMA) for purposes of FCM design, which is compared also to other methods like particle swarm optimization, simulated annealing, active and nonlinear Hebbian learning on experiments with catching targets for future purposes of robotic soccer. Obtained results are compared where advantages of the proposed method are apparent and in the conclusions their properties are summarized. Besides, a new modification of FCM with active inputs is presented that is able to receive data from sensors in each time step. © 2012 by IJAI (CESER Publications).
AUTHOR KEYWORDS: Fuzzy cognitive maps; Migration Algorithm; Navigation; Self-learning
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Axelrod, R., Structure of Decision (1976) The Cognitive Maps of Political Elites, , Princeton University Press;
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Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M., Learning fuzzy cognitive maps using evolution strategies: A novel schema for modeling and simulating high-level behavior (2001) Proc. of the 2001 Congress on Evolutionary Computation, Seoul, 1, pp. 364-371;
LaValle, S.M., (2006) Planning Algorithms, , http://planning.cs.uiuc.edu/, Cambridge University Press, Cambridge, U.K. Available at;
Li, S.J., Shen, R.M., Fuzzy cognitive map learning based on improved nonlinear hebbian rule (2004) Proc. of the Third International Conference on Machine Learning and Cybernetics, Shanghai, pp. 2301-2306;
Lijuan, Z., Zhangming, L., Optimal selection of design schemes for a sparse distributed pile foundation based on fuzzy optimization theory (2009) Kybernetes, 38 (10), pp. 1828-1834;
Nolle, L., Zelinka, I., Hopgooda, A.A., Goodyear, A., Comparison of an self-organizing migration algorithm with simulated annealing and differential evolution for automated waveform tuning (2005) Advances in Engineering Software, 36 (10), pp. 645-653;
Oblak, S., Skrjanc, I., Blazic, S., If approximating nonlinear areas, then consider fuzzy systems (2006) IEEE Potentials, 25 (6), pp. 18-23;
Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.D., Groumpos, P.P., Vrahatis, M.N., Fuzzy cognitive maps learning using particle swarm optimization (2005) International Journal of Intelligent Information Systems, 25 (1), pp. 95-121;
Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P., Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal link (2006) Int. Journal of Human-Computer Studies, 64 (8), pp. 727-743;
Pozna, C., Troester, F., Precup, R.E., Tar, J.K., Preitl, S., On the design of an obstacle avoiding trajectory: Method and simulation (2009) Mathematics and Computers in Simulation, 79 (7), pp. 2211-2226;
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M., Genetic learning of fuzzy cognitive maps (2005) Fuzzy Sets and Systems, 153 (3), pp. 371-401;
Vǎšcák, J., Hirota, K., Integrated decision-making system for robot soccer (2011) Journal of Advanced Computational Intelligence and Intelligent Informatics, 15 (2), pp. 156-163;
Vǎšcák, J., Evolutionary migration algorithms for scheduling (2005) Proc. of the 3th International Symposium on Applied Machine Intelligence and Informatics, pp. 21-32;
Vǎšcák, J., Madarász, L., Adaptation of fuzzy cognitive maps-a comparison study (2010) Acta Polytechnica Hungarica, 7 (3), pp. 109-122;
Zelinka, I., (2002) Artificial Intelligence in Problems of Global Optimization, , BEN, Prague. in Czech
DOCUMENT TYPE: Article
SOURCE: Scopus
Daneshyari, M.a , Yen, G.G.b
Constrained multiple-swarm particle swarm optimization within a cultural framework
(2012) IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 42 (2), art. no. 5979161, pp. 475-490.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857502646&partnerID=40&md5=66992ae5ea78d380e041392a64eef564
AFFILIATIONS: Department of Technology, Elizabeth City State University, Elizabeth City, NC 27909, United States;
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, United States
ABSTRACT: Particle swarm optimization (PSO) has been recently adopted to solve constrained optimization problems. In this paper, a cultural-based constrained PSO is proposed to incorporate the information of the objective function and constraint violation into four sections of the belief space, specifically normative knowledge, spatial knowledge, situational knowledge, and temporal knowledge. The archived information facilitates communication among swarms in the population space and assists in selecting the leading particles in three different levels: personal, swarm, and global levels. Comprehensive comparison of the proposed heuristics over a number of benchmark problems with selected state-of-the-art constraint-handling techniques demonstrates that the proposed cultural framework helps the multiple-swarm PSO to perform competitively with respect to selected designs. © 2011 IEEE.
AUTHOR KEYWORDS: Constrained optimization; constrained particle swarm optimization (CPSO); cultural algorithm (CA); PSO
REFERENCES: Venkatraman, S., Yen, G.G., A generic framework for constrained optimization using genetic algorithms (2005) IEEE Transactions on Evolutionary Computation, 9 (4), pp. 424-435., DOI 10.1109/TEVC.2005.846817;
Wang, Y., Jiao, Y.-C., Li, H., An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handling scheme (2005) IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 35 (2), pp. 221-232., DOI 10.1109/TSMCC.2004.841908;
Grefenstette, J., Optimization of control parameters for genetic algorithms (1986) IEEE Trans. Syst., Man, Cybern., SMC-16 (1), pp. 122-128., Jan;
Kennedy, J., Eberhart, R.C., Particle swarm optimization (1995) Proc. Int. Joint Conf. Neural Netw., pp. 1942-1948., Perth, Australia;
Clerc, M., Kennedy, J., The particle swarm-explosion, stability, and convergence in a multidimensional complex space (2002) IEEE Transactions on Evolutionary Computation, 6 (1), pp. 58-73., DOI 10.1109/4235.985692, PII S1089778X02022099;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Lee, K.C.a , Lee, N.b , Lee, H.c
Multi-agent knowledge integration mechanism using particle swarm optimization
(2012) Technological Forecasting and Social Change, 79 (3), pp. 469-484.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84856719483&partnerID=40&md5=901cd059fd2bf9e0fd461760d6b365b2
AFFILIATIONS: SKK Business School, WCU Professor at Department of Interaction Science, Sungkyunkwan University, Seoul 110-745, South Korea;
EMC Korea, Yeoksam 1-737, Kangnam-Ku, Seoul 135-984, South Korea;
Brunel Business School, Brunel University, Uxbridge, United Kingdom
ABSTRACT: Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust. © 2011 Elsevier Inc.
AUTHOR KEYWORDS: Agent-based model (ABM); Expert knowledge; Fuzzy cognitive map (FCM); IT project risk assessment; Knowledge integration; Particle swarm optimization (PSO)
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DOCUMENT TYPE: Article
SOURCE: Scopus
Li, M., Lin, D., Kou, J.
A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization
(2012) Applied Soft Computing Journal, 12 (3), pp. 975-987. Cited 1 time.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84855990520&partnerID=40&md5=a3fb97bc0b30af16eb2f44b3d43a8abb
AFFILIATIONS: School of Management, Tianjin University, Tianjin 300072, China
ABSTRACT: This paper presents a hybrid niching algorithm based on the PSO to deal with multimodal function optimization problems. First, we propose to evolve directly both the particle population and memory population (archive population), called the P&A pattern, to enhance the efficiency of the PSO for solving multimodal optimization functions, and investigate illustratively the niching capability of the PSO and the PSO
P&A. It is found that the global version PSO is disable, but the local version PSO
P&A is able, to niche multiple species for locating multiple optima. Second, the recombination-replacement crowding strategy that works on the archive population is introduced to improve the exploration capability, and the hybrid niching PSO
P&A (HN-PSO
P&A) is developed. Finally, experiments are carried out on multimodal functions for testing the niching efficiency and scalability of the proposed method, and it is verified that the proposed method has a sub-quadratic scalability with dimension in terms of fitness function evaluations on specific MMFO problems. © 2011 Elsevier B.V. All rights reserved.
AUTHOR KEYWORDS: Multimodal function optimization; Niching; Particle swarm optimization; Recombination-replacement crowding
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Li, X., Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization (2004) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3102, pp. 105-116;
Goldberg, D.E., (1989) Genetic Algorithms in Search Optimization and Machine Learning, pp. 185-197., Addison-Wesley Publishing Company New York;
Mahfoud, S.W., (1995) Niching Methods for Genetic Algorithms, , Doctoral Dissertation. University of Illinois at Urbana-Champaign, Urbana, IL also IlliGAL Report No. 95001;
Horn, J., (1997) The Nature of Niching: Genetic Algorithms and the Evolution of Optimal, Cooperative Populations, , Doctoral Dissertation. University of Illinois at Urbana-Champaign, Urbana, IL also IlliGAL Report No. 97008;
Eiben, A.E., Smith, J.E., (2003) Introduction to Evolutionary Computing, , Springer-Verlag Berlin, Heidelberg;
Goldberg, D.E., (2002) Design of Innovation: Lessons from and for Competent Genetic Algorithms, , Kluwer Academic Publishers Boston, MA;
Goldberg, D.E., Real-coded genetic algorithms, virtual alphabets, and blocking (1991) Complex Systems, 5 (2), pp. 139-167;
Li, J.-P., Balazs, M.E., Parks, G.T., Clarkson, P.J., A species conserving genetic algorithm for multimodal function optimization (2002) Evolutionary Computation, 10 (3), pp. 207-234;
Peer, E.S., Van Den Bergh, F., Engelbrecht, A.P., Using neighborhoods with guaranteed convergence PSO (2003) Proceedings of the IEEE Swarm Intelligence Symposium (SIS 03), pp. 235-242., Indianapolis, USA, 24-26 April 2003
DOCUMENT TYPE: Article
SOURCE: Scopus
Ghosh, S.a , Das, S.a , Kundu, D.a , Suresh, K.a , Panigrahi, B.K.b , Cui, Z.c
An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization
(2012) Neural Computing and Applications, 21 (2), pp. 237-250.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857656487&partnerID=40&md5=1182dbc8bae9032ef61a5050fc285a9a
AFFILIATIONS: Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;
Department of Electrical Engineering, IIT, Delhi, India;
Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China
ABSTRACT: Particle Swarm Optimization (PSO) has recently emerged as a nature-inspired algorithm for real parameter optimization. This article describes a method for improving the final accuracy and the convergence speed of PSO by firstly adding a new coefficient (called mobility factor) to the position updating equation and secondly modulating the inertia weight according to the distance between a particle and the globally best position found so far. The two-fold modification tries to balance between the explorative and exploitative tendencies of the swarm with an objective of achieving better search performance. We also mathematically analyze the effect of the modifications on the dynamics of the PSO algorithm. The new algorithm has been shown to be statistically significantly better than the basic PSO and four of its state-of-the-art variants on a twelve-function test-suite in terms of speed, accuracy, and robustness. © 2010 Springer-Verlag London Limited.
AUTHOR KEYWORDS: Benchmark functions; Comprehensive learning; Dynamics of swarm; Global optimization; Particle swarm systems; Swarm intelligence
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DOCUMENT TYPE: Article
SOURCE: Scopus
Deb, K.a b c , Saha, A.b
Multimodal optimization using a bi-objective evolutionary algorithm
(2012) Evolutionary Computation, 20 (1), pp. 27-62.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84857519155&partnerID=40&md5=f13eeef51c206d0110340a6b9d489a34
AFFILIATIONS: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States;
Kanpur Genetic Algorithms Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208 016, India;
Department of Information and Service Economy, Aalto University School of Economics, Helsinki, Finland
ABSTRACT: In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable biobjective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper,we have solved up to 16-variable test problems having asmany as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application. © 2012 by the Massachusetts Institute of Technology.
AUTHOR KEYWORDS: Bi-objective optimization; Hooke-Jeeves exploratory search; Multimodal constrained optimization; Multimodal optimization; NSGA-II
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DOCUMENT TYPE: Article
SOURCE: Scopus
Wang, H.
Theoretical analysis of adaptive Cauchy mutation in particle swarm optimization
(2012) Journal of Computational Information Systems, 8 (3), pp. 1325-1332. Cited 1 time.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84863338256&partnerID=40&md5=524aed2e398ea60ea4fc13f9e30f3d32
AFFILIATIONS: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
ABSTRACT: Swarm Optimization (PSO) has shown fast convergence rate in solving many benchmark and real-world problems. However, PSO easily falls into local minima when solving complex multimodal problems. To overcome the premature convergence and avoid local minima, an adaptive Cauchy mutation operator is proposed to help trapped particles jump to better positions. Theoretical studies demonstrate that the adaptive Cauchy mutation is beneficial for generating near-optimal solutions. 1553-9105/Copyright © 2012 Binary Information Press.
AUTHOR KEYWORDS: Cauchy mutation; Evolutionary computation; Particle swarm optimization; Theoretical study
REFERENCES: Kennedy, J., Eberhart, R.C., Particle swarm optimization (1995) Proc. of IEEE Int. Conf. Neural Networks, pp. 1942-1948;
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DOCUMENT TYPE: Article
SOURCE: Scopus
Li, C., Hu, J.-W.
A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting
(2012) Engineering Applications of Artificial Intelligence, 25 (2), pp. 295-308.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84855819427&partnerID=40&md5=1f3f20c197fdcbec4ed92e513f7fd0fc
AFFILIATIONS: Laboratory of Intelligent Systems and Applications, Department of Information Management, National Central University, Taiwan
ABSTRACT: Time series forecasting is an important and widely interesting topic in the research of system modeling. We propose a new computational intelligence approach to the problem of time series forecasting, using a neuro-fuzzy system (NFS) with auto-regressive integrated moving average (ARIMA) models and a novel hybrid learning method. The proposed intelligent system is denoted as the NFSARIMA model, which is used as an adaptive nonlinear predictor to the forecasting problem. For the NFSARIMA, the focus is on the design of fuzzy If-Then rules, where ARIMA models are embedded in the consequent parts of If-Then rules. For the hybrid learning method, the well-known particle swarm optimization (PSO) algorithm and the recursive least-squares estimator (RLSE) are combined together in a hybrid way so that they can update the free parameters of NFSARIMA efficiently. The PSO is used to update the If-part parameters of the proposed predictor, and the RLSE is used to adapt the Then-part parameters. With the hybrid PSORLSE learning method, the NFSARIMA predictor may converge in fast learning pace with admirable performance. Three examples are used to test the proposed approach for forecasting ability. The results by the proposed approach are compared to other approaches. The performance comparison shows that the proposed approach performs appreciably better than the compared approaches. Through the experimental results, the proposed approach has shown excellent prediction performance. © 2011 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Auto-regressive integrated moving average model (ARIMA); Hybrid learning; Neuro-fuzzy system (NFS); Particle swarm optimization (PSO); Recursive least-squares estimator (RLSE); Time series forecasting
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DOCUMENT TYPE: Article
SOURCE: Scopus
Jeung, H.-S., Choi, H.-G.
Particle swarm optimization in multi-stage operations for operation sequence and DT allocation
(2012) Computers and Industrial Engineering, 62 (2), pp. 442-450.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84855764271&partnerID=40&md5=6f0782b95f0b3413bfa9835272b117c1
AFFILIATIONS: Department of Systems Management Engineering, Sungkyunkwan University, Suwon 440-746, South Korea
ABSTRACT: Improved operation sequence and economic tolerance allocation directly influence product quality and manufacturing costs. The purpose of this study is to generate the optimal operation sequence and allocate economic tolerances to cutting surfaces to achieve the specified quality and minimize the manufacturing costs. Because this type of problem is a multi-objective optimization problem subject to various constraints, it is defined as an NP-hard problem. A three-step procedure is used to solve the problem. First, a mathematical model is developed to define the relationships between manufacturing costs and tolerances. Second, an artificial neural network (ANN) is applied to obtain the best fitting cost-tolerance function. Finally, the formulated mathematical models are solved by using particle swarm optimization (PSO) in order to determine the optimal operation sequence. In addition, both the effectiveness and efficiency of the proposed methodologies are tested and verified for a given workpiece that needs multi-stage operations. The key contributions of this study are the generation of the optimal operation sequence and the effective allocation of the optimal dimensional tolerance (DT) using an advanced computational intelligence algorithm with consideration for multi-stage operations. © 2011 Elsevier Ltd. All rights reserved.
AUTHOR KEYWORDS: Dimensional tolerance (DT) allocation; Multi-stage operations; Neural network; Operation sequence generation; Particle swarm optimization
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DOCUMENT TYPE: Article
SOURCE: Scopus
Baykasoglu, A.
Design optimization with chaos embedded great deluge algorithm
(2012) Applied Soft Computing Journal, 12 (3), pp. 1055-1067.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84856005376&partnerID=40&md5=ab27f058112722cd68dcdf3ccf73f828
AFFILIATIONS: Dokuz Eylul University, Faculty of Engineering, Department of Industrial Engineering, 35160 Izmir, Turkey
ABSTRACT: In this paper, the great deluge algorithm (GDA), which has not been previously used in constrained mechanical design optimization problems is employed to solve several design optimization problems selected from the literature. The GDA algorithm needs only one basic parameter to setup, which makes it very attractive for solving optimization problems. First time in this paper, an attempt is made to see whether it is possible to enhance the performance of a very simple algorithm like GDA to solve complex constrained non-linear design optimization problems by embedding chaotic maps in its neighborhood generation mechanism. Eight different chaotic maps are tested and compared in this paper. It is observed that chaotic maps can considerably improve the performance of GDA and enables it to find the best possible solutions for the studied problems. © 2011 Elsevier B.V. All rights reserved.
AUTHOR KEYWORDS: Chaotic maps; Design optimization; Great deluge algorithm; Non-linear programming
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DOCUMENT TYPE: Article
SOURCE: Scopus
Matott, L.S.
Screening-Level Sensitivity Analysis for the Design of Pump-and-Treat Systems
(2012) Ground Water Monitoring and Remediation, 32 (2), pp. 66-80.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84860643863&partnerID=40&md5=6c73ece5cc2c4c1fd9e6b4856d85d535
AFFILIATIONS: Center for Computational Research University at Buffalo, 701 Ellicott Street, Buffalo, NY 14203, United States
ABSTRACT: Pump-and-treat systems can prevent the migration of groundwater contaminants and candidate systems are typically evaluated with groundwater models. Such models should be rigorously assessed to determine predictive capabilities and numerous tools and techniques for model assessment are available. While various assessment methodologies (e.g., model calibration, uncertainty analysis, and Bayesian inference) are well-established for groundwater modeling, this paper calls attention to an alternative assessment technique known as screening-level sensitivity analysis (SLSA). SLSA can quickly quantify first-order (i.e., main effects) measures of parameter influence in connection with various model outputs. Subsequent comparisons of parameter influence with respect to calibration vs. prediction outputs can suggest gaps in model structure and/or data. Thus, while SLSA has received little attention in the context of groundwater modeling and remedial system design, it can nonetheless serve as a useful and computationally efficient tool for preliminary model assessment. To illustrate the use of SLSA in the context of designing groundwater remediation systems, four SLSA techniques were applied to a hypothetical, yet realistic, pump-and-treat case study to determine the relative influence of six hydraulic conductivity parameters. Considered methods were: Taguchi design-of-experiments (TDOE); Monte Carlo statistical independence (MCSI) tests; average composite scaled sensitivities (ACSS); and elementary effects sensitivity analysis (EESA). In terms of performance, the various methods identified the same parameters as being the most influential for a given simulation output. Furthermore, results indicate that the background hydraulic conductivity is important for predicting system performance, but calibration outputs are insensitive to this parameter (K
BK). The observed insensitivity is attributed to a nonphysical specified-head boundary condition used in the model formulation which effectively "staples" head values located within the conductivity zone. Thus, potential strategies for improving model predictive capabilities include additional data collection targeting the K
BK parameter and/or revision of model structure to reduce the influence of the specified head boundary. © 2011, The Author(s). Ground Water Monitoring & Remediation © 2011, National Ground Water Association.
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DOCUMENT TYPE: Article
SOURCE: Scopus
Parejo, J.A., Ruiz-Cortés, A., Lozano, S., Fernandez, P.
Metaheuristic optimization frameworks: A survey and benchmarking
(2012) Soft Computing, 16 (3), pp. 527-561.
http://www.scopus.com/inward/record.url?eid=2-s2.0-84856954883&partnerID=40&md5=89a565c7021efebefa8bc28d73cca94b
AFFILIATIONS: University of Sevilla, Seville, Spain
ABSTRACT: This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed. © 2011 Springer-Verlag.
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