Nature-Inspired Optimization Algorithms
(Sprache: Englisch)
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,...
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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.
Autoren-Porträt von Xin-She Yang
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).
Bibliographische Angaben
- Autor: Xin-She Yang
- Maße: 15,2 x 22,9 cm, Gebunden, Englisch
- Verlag: Elsevier Science & Technology
- ISBN-10: 0124167438
- ISBN-13: 9780124167438
- Erscheinungsdatum: 20.02.2014
Sprache:
Englisch
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