Nature-Inspired Optimization Algorithms

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Elsevier Science Limited, 2014 - COMPUTERS - 263 pages
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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 (2014)

Xin-She Yang received his DPhil in applied mathematics from the University of Oxford. He is currently a research fellow at the Univer-sity of Cambridge. He is also the author of the book "An Introduction to Computational Engineering With Matlab (CISP, 2006).

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