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Nature-Inspired Optimization Algorithms

by Xin-She Yang
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Current price ₹8,442.00
Original price ₹14,070.00
Original price ₹14,070.00
Original price ₹14,070.00
(-40%)
₹8,442.00
Current price ₹8,442.00

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Book cover type: Paperback
  • ISBN13: 9780128219867
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Academic Press
  • Publisher Imprint: Academic Press
  • Publication Date:
  • Pages: 310
  • Original Price: USD 150.0
  • Language: English
  • Edition: 2nd Ed.
  • Item Weight: 545 grams
  • BISAC Subject(s): Biotechnology and Biomedical

Nature-Inspired Optimization Algorithms, Second Edition provides an 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 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, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.

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 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).

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