
Metaheuristic Computation with MATLAB (R)
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 23. July 2020
Book
Hardback
280 pages
978-0-367-43886-9 (ISBN)
Description
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 (R)
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.
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 (R)
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.
More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
3 s/w Tabellen, 100 s/w Abbildungen
3 Tables, black and white; 100 Illustrations, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 20 mm
Weight
730 gr
ISBN-13
978-0-367-43886-9 (9780367438869)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Erik Cuevas | Alma Rodriguez
Metaheuristic Computation with MATLAB (R)
Book
05/2022
1st Edition
Chapman & Hall/CRC
€70.60
Shipment within 15-20 days

Erik Cuevas | Alma Rodriguez
Metaheuristic Computation with MATLAB (R)
E-Book
09/2020
1st Edition
Chapman & Hall/CRC
€65.99
Available for download

Erik Cuevas | Alma Rodriguez
Metaheuristic Computation with MATLAB (R)
E-Book
09/2020
1st Edition
Chapman & Hall/CRC
€65.99
Available for download
Persons
Erik Cuevas is a professor in the Department of Electronics at the University of Guadalajara, Mexico.
Alma Rodriguez is a PhD candidate in electronics and computer science at the University of Guadalajara, Mexico.
Alma Rodriguez is a PhD candidate in electronics and computer science at the University of Guadalajara, Mexico.
Content
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.