Nature-Inspired Optimization Algorithms

by Xin-She Yang

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.

310 pages, Paperback

First published January 1, 2014

Reviews

[Adam Chehouri · Rating 4 out of 5]

Nature-Inspired Optimization Algorithms provides an introduction to all major nature-inspired algorithms for optimization. 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.
I would recommend this book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It could also serve as a source of inspiration for new applications.