Elsevier Insights) Xin-She Yang (Auth.) - Nature-Inspired Optimization Algorithms-Elsevier (2014).pdf (12.3 mb)
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 algorithm
Graduates, PhD students and lecturers in computer science, engineering and natural sciences and also researchers and engineers.
Table of Contents
1: Introduction to Algorithms View more >
No. of pages:
© Elsevier 2014
20th February 2014
About the Author
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).
Affiliations and Expertise
School of Science and Technology, Middlesex University, UK
"...the book is well written and easy to follow, even for algorithmic and mathematical laymen. Since the book focuses on optimization algorithms, it covers a very important and actual topic." --IEEE Communications Magazine, Nature-Inspired Optimization Algorithms View more >
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