Metaheuristic algorithms are considered generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Metaheuristic Computation with MATLAB® provides a unified view of the most popular metaheuristic methods currently in use.
The material has been written from a teaching perspective and for this reason, the book is primarily intended for undergraduate and postgraduate students of Artificial Intelligence, Metaheuristic methods and/or Evolutionary Computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit on the convenient properties of metaheuristic approaches. Therefore, engineer practitioners, who are not familiar with metaheuristic computation, will appreciate that the techniques discussed are beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise in such areas.
Chapter 1 Introduction and main concepts
Chapter 2 Genetic algorithms (GA)
Chapter 3 Evolutionary Strategies (ES)
Chapter 4 Moth Flame Optimization (MFO)
Chapter 5 Differential Evolution (DE)
Chapter 6 Particle Swarm Optimization Algorithm (PSO)
Chapter 7 Artificial Bee Colony (ABC)
Chapter 8 Cuckoo Search Algorithm (CS)
Chapter 9 Multimodal techniques