Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms

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Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.
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Product details

  • Hardback | 300 pages
  • 156 x 232 x 22mm | 519.99g
  • United States
  • English
  • black & white illustrations
  • 0124167438
  • 9780124167438
  • 1,771,704

Review quote

"...the book is well written and easy to follow, even for algorithmic and mathematical laymen. Since the book focuses on optimization algorithms, it covers a very important and actual topic."--IEEE Communications Magazine, Nature-Inspired Optimization Algorithms "...this book strives to introduce the latest developments regarding all major nature-inspired algorithms..." -, August 2014
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Table of contents

1. Overview of Modern Nature-Inspired Algorithms 2. Particle Swarm Optimization 3. Genetic Algorithms and Differential Evolution 4. Simulated Annealing 5. Ant Colony Optimization 6. Artificial Bee Colony and Other Bee Algorithms 7. Cuckoo Search 8. Firefly Algorithm 9. Artificial Immune Systems 10. Bat Algorithms 11. Neural Networks 12. Other Optimization Algorithms 13. Constraint Handling Techniques 14. Multiobjective Optimization Appendix A: Matlab Codes and Some Software Links Appendix B: Commonly used test functions
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About Xin-She Yang

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi'an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).
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Rating details

3 ratings
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