Weekly Hours: 5
ECTS Credits: 5
Description: The course deals with modern Computational Optimization methods from the fields of Evolutionary Computation and Swarm Intelligence. In their majority, these methods stemmed from models of optimization processes observed in nature, using mathematical tools from Probability Theory and Dynamical Systems. Although, the presence of strict mathematical conditions is not required for their application. This attribute renders them capable of tackling optimization problems where there are no objective functions in analytical form with desirable mathematical characteristics, such as continuity and differentiability, or problems where the model is contaminated by noise and/or loss of information. Moreover, their inherent parallelization properties renders these algorithms suitable for computationally expensive problems. In this framework, we present the basic concepts of methods such as Genetic Algorithms, Evolutionary Algorithms, Particle Swarm Optimization, Differential Evolution, Harmony Search and Ant Colony Optimization. Also, several applications are presented with an emphasis on Global Optimization problems in various scientific and technological fields such as Operations Research, Astrophysics, Mechanics etc.