Xin-She Yang

Nature-Inspired Optimization Algorithms

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Elsevier / Elsevier Science & Technology
• Artikelnr. des Verlages: C2013-0-01368-0
• Englisch
• Abmessung: 12mm x 152mm x 229mm
• Gewicht: 454g
• ISBN-13: 9780128100608
• Artikelnr.: 45101193

Autorenporträt

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).

Inhaltsangabe

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

Rezensionen

"...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