Metaheuristic algorithms are considered as 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.
- Provides a unified view of the most popular metaheuristic methods currently in use
- Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems
- Covers design aspects and implementation in MATLAB®
- Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization
The material has been written from a teaching perspective and, for this reason, this 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 from 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.
Table of Contents
Preface. Acknowledgments. Authors. Chapter 1 Introduction and Main Concepts. Chapter 2 Genetic Algorithms (GA). Chapter 3 Evolutionary Strategies (ES). Chapter 4 Moth–Flame Optimization (MFO) Algorithm. Chapter 5 Differential Evolution (DE). Chapter 6 Particle Swarm Optimization (PSO) Algorithm. Chapter 7 Artificial Bee Colony (ABC) Algorithm. Chapter 8 Cuckoo Search (CS) Algorithm. Chapter 9 Metaheuristic Multimodal Optimization. Index.
Erik Cuevas is a professor in the Department of Electronics at the University of Guadalajara, Mexico.
Alma Rodríguez is a PhD candidate in electronics and computer science at the University of Guadalajara, Mexico.