Nature-Inspired Optimization Algorithms - Product Image

Nature-Inspired Optimization Algorithms

  • ID: 3627001
  • Book
  • 300 Pages
  • Elsevier Science and Technology
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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.

  • Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
  • Provides a theoretical understanding as well as practical implementation hints
  • Provides a step-by-step introduction to each algorithm
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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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Yang, Xin-She
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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