Nature-Inspired Optimization Algorithms (Paperback)

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.

General

Imprint:	Elsevier Science Publishing Co Inc
Country of origin:	United States
Release date:	August 2016
First published:	2014
Authors:	Xin-She Yang
Dimensions:	229 x 152 x 15mm (L x W x T)
Format:	Paperback
Pages:	300
ISBN-13:	978-0-12-810060-8
Categories:	Books > Science & Mathematics > Mathematics > Optimization > General
LSN:	0-12-810060-5
Barcode:	9780128100608

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