Search and Optimization by Metaheuristics
Techniques and Algorithms Inspired by Nature
Authors: Du, Ke-Lin, Swamy, M. N. S.
Free Preview- Offers a comprehensive and state-of-the-art introduction to nature-inspired metaheuristics
- Includes detailed, implementable algorithmic flowcharts for the most popular algorithms
- Discusses over 100 different types of nature-inspired search and optimization methods
- Will allow students to discover the newest trends in metaheuristics and optimization
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- About this Textbook
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This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones.
An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics.
Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods. - About the authors
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Ke-Lin Du, PhD, is Affiliate Associate Professor at Concordia University, Montreal, Quebec, Canada, and Founder and CEO of Xonlink Inc, Ningbo, China.
M.N.S. Swamy, PhD, is Research Professor and Tier I Concordia Research Chair in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Quebec, Canada. - Reviews
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“The book under review contains large amount of precisely selected topics covering various aspects and design techniques related to efficient metaheuristic algorithms for searching and optimization. … is intended primarily as a textbook for graduate students specializing in engineering and computer science. Besides being very useful as a valuable resource for post-docs and researchers working in these areas, it may as well be used by those who are interested in search and optimization methods in general.” (Vladimír Lacko, zbMATH, 1351.90002, 2017)
- Table of contents (23 chapters)
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Introduction
Pages 1-28
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Simulated Annealing
Pages 29-36
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Genetic Algorithms
Pages 37-69
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Genetic Programming
Pages 71-82
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Evolutionary Strategies
Pages 83-91
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Table of contents (23 chapters)
- Download Preface 1 PDF (64.7 KB)
- Download Sample pages 2 PDF (211.9 KB)
- Download Table of contents PDF (249.2 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Search and Optimization by Metaheuristics
- Book Subtitle
- Techniques and Algorithms Inspired by Nature
- Authors
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- Ke-Lin Du
- M. N. S. Swamy
- Copyright
- 2016
- Publisher
- Birkhäuser Basel
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-41192-7
- DOI
- 10.1007/978-3-319-41192-7
- Hardcover ISBN
- 978-3-319-41191-0
- Softcover ISBN
- 978-3-319-82290-7
- Edition Number
- 1
- Number of Pages
- XXI, 434
- Number of Illustrations
- 28 b/w illustrations, 40 illustrations in colour
- Topics
