Nature-Inspired Optimization Algorithms

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 in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).

• 1. Overview of Modern Nature-Inspired Algorithms2. Particle Swarm Optimization 3. Genetic Algorithms and Differential Evolution4. Simulated Annealing5. Ant Colony Optimization 6. Artificial Bee Colony and Other Bee Algorithms7. Cuckoo Search8. Firefly Algorithm9. Artificial Immune Systems10. Bat Algorithms 11. Neural Networks12. Other Optimization Algorithms 13. Constraint Handling Techniques14. Multiobjective Optimization Appendix A: Matlab Codes and Some Software LinksAppendix B: Commonly used test functions

"...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…" - HPCMagazine.com, August 2014