Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms

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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 algorithmRecent studies have demonstrated that cuckoo search can perform significantly better than other algorithms in many applications [13, 17, 37, 36]. For a more detailed review, refer ... Nature Figure 10.1 Pseudo code of the bat algorithm (BA) . 10.5.

Title:Nature-Inspired Optimization Algorithms
Author:Xin-She Yang
Publisher:Elsevier - 2014-02-17


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