Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature

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Birkhäuser, Jul 20, 2016 - Computers - 434 pages
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.
 

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Contents

1 Introduction
1
2 Simulated Annealing
29
3 Genetic Algorithms
37
4 Genetic Programming
71
5 Evolutionary Strategies
83
6 Differential Evolution
93
7 Estimation of Distribution Algorithms
105
8 Topics in Evolutinary Algorithms
121
14 Harmony Search
227
15 Swarm Intelligence
236
16 Biomolecular Computing
265
17 Quantum Computing
282
18 Metaheuristics Based on Sciences
295
19 Memetic Algorithms
315
20 Tabu Search and Scatter Search
326
21 Search Based on Human Behaviors
337

9 Particle Swarm Optimization
153
10 Artificial Immune Systems
174
11 Ant Colony Optimization
191
12 Bee Metaheuristics
200
13 Bacterial Foraging Algorithm
217
22 Dynamic Multimodal and Constrained Optimizations
347
23 Multiobjective Optimization
371
A Appendix Benchmarks
413
Index
430
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About the author (2016)

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.

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