Description: Nature-Inspired Optimization Algorithms A theoretical and practical introduction to all major nature-inspired algorithms for optimization Xin-She Yang (Author) 9780128100608 Paperback / softback, published 19 August 2016 300 pages 22.9 x 15.2 x 2 cm, 0.41 kg "...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 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. 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 Subject Areas: Algorithms & data structures [UMB], Information theory [GPF]
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BIC Subject Area 1: Algorithms & data structures [UMB]
BIC Subject Area 2: Information theory [GPF]
Number of Pages: 300 Pages
Language: English
Publication Name: Nature-Inspired Optimization Algorithms
Publisher: Elsevier Science Publishing Co INC International Concepts
Publication Year: 2016
Subject: Technology, Computer Science
Item Height: 229 mm
Item Weight: 410 g
Type: Textbook
Author: Xin-She Yang
Item Width: 152 mm
Format: Paperback
