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, artificial immune system, harmony search, artificial neural networks, 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. It discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature. It provides a theoretical understanding as well as practical implementation hints. It provides a step-by-step introduction to each algorithm.
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"...this book strives to introduce the latest developments regarding all major nature-inspired algorithms." - HPCMagazine.com, August 2014About the Author:
Xin She Yang is Senior Research Scientist in the Department of Mathematical and Scientific Computing at the National Physical Laboratory in the United Kingdom, Reader in Modeling and Optimization at Middlesex University, UK, and Adjunct Professor at Reykjavik University, Iceland. He is Editor-in-Chief of the International Journal of Mathematical Modelling and Numerical Optimization, a member of both the Society for Industrial and Applied Mathematics and the British Computer Society, a Fellow of The Royal Institution of Great Britain, and editor of seven additional books including Nature-Inspired Optimization Algorithms (Elsevier), Swarm Intelligence and Bio-Inspired Computation (Elsevier).
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Book Description Elsevier, 2014. Hardcover. Book Condition: New. book. Bookseller Inventory # 0124167438
Book Description Elsevier, 2014. Hardcover. Book Condition: New. Bookseller Inventory # P110124167438
Book Description Elsevier Science Ltd, 2014. Hardcover. Book Condition: Brand New. 263 pages. 9.25x6.25x1.00 inches. In Stock. Bookseller Inventory # zk0124167438