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Search and Optimization by Metaheuristics

Techniques and Algorithms Inspired by Nature

  • Ke-Lin Du
  • M. N. S. Swamy

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Ke-Lin Du, M. N. S. Swamy
    Pages 1-28
  3. Ke-Lin Du, M. N. S. Swamy
    Pages 29-36
  4. Ke-Lin Du, M. N. S. Swamy
    Pages 37-69
  5. Ke-Lin Du, M. N. S. Swamy
    Pages 71-82
  6. Ke-Lin Du, M. N. S. Swamy
    Pages 83-91
  7. Ke-Lin Du, M. N. S. Swamy
    Pages 93-103
  8. Ke-Lin Du, M. N. S. Swamy
    Pages 105-119
  9. Ke-Lin Du, M. N. S. Swamy
    Pages 121-152
  10. Ke-Lin Du, M. N. S. Swamy
    Pages 153-173
  11. Ke-Lin Du, M. N. S. Swamy
    Pages 175-189
  12. Ke-Lin Du, M. N. S. Swamy
    Pages 191-199
  13. Ke-Lin Du, M. N. S. Swamy
    Pages 201-216
  14. Ke-Lin Du, M. N. S. Swamy
    Pages 217-225
  15. Ke-Lin Du, M. N. S. Swamy
    Pages 227-235
  16. Ke-Lin Du, M. N. S. Swamy
    Pages 237-263
  17. Ke-Lin Du, M. N. S. Swamy
    Pages 265-281
  18. Ke-Lin Du, M. N. S. Swamy
    Pages 283-293
  19. Ke-Lin Du, M. N. S. Swamy
    Pages 295-314
  20. Ke-Lin Du, M. N. S. Swamy
    Pages 315-325
  21. Ke-Lin Du, M. N. S. Swamy
    Pages 327-336
  22. Ke-Lin Du, M. N. S. Swamy
    Pages 337-346
  23. Ke-Lin Du, M. N. S. Swamy
    Pages 347-369
  24. Ke-Lin Du, M. N. S. Swamy
    Pages 371-412
  25. Back Matter
    Pages 413-434

About this book

Introduction

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.

Keywords

Metaheuristics Evolutionary Computation Natural Computing Swarm Intelligence Optimization

Authors and affiliations

  • Ke-Lin Du
    • 1
  • M. N. S. Swamy
    • 2
  1. 1.Xonlink Inc, Ningbo, Zhejiang, China, and Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

Bibliographic information