Metaheuristic Computation with MATLAB® - Erik Cuevas, Alma Rodriguez

Metaheuristic Computation with MATLAB®

Buch | Softcover
280 Seiten
2022
Chapman & Hall/CRC (Verlag)
978-0-367-52380-0 (ISBN)
56,10 inkl. MwSt
The main purpose of this book is to provide a unified view of the most popular metaheuristic methods. Under this perspective, it has presented the fundamental design principles as well as the operators of metaheuristic approaches which are considered essential.
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.

Book Features:






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.

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.

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.

Erscheinungsdatum
Zusatzinfo 3 Tables, black and white; 100 Illustrations, black and white
Sprache englisch
Maße 178 x 254 mm
Gewicht 453 g
Themenwelt Informatik Theorie / Studium Algorithmen
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
ISBN-10 0-367-52380-9 / 0367523809
ISBN-13 978-0-367-52380-0 / 9780367523800
Zustand Neuware
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