Items related to Nature-Inspired Optimization Algorithms (Elsevier Insights)
Synopsis
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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About the Author
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).
"About this title" may belong to another edition of this title.
- PublisherElsevier
- Publication date2014
- ISBN 10 0124167438
- ISBN 13 9780124167438
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages300
- Rating
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