Nature-Inspired Optimization Algorithms

By: Yang

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Paperback | 19 August 2016 | Edition Number 1

At a Glance

Paperback
276 Pages

Dimensions(cm)
22.86 x 15.24 x 1.47

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Published: 14th September 2020

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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

Industry Reviews

"...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

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