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Introduction to Nature-Inspired Optimization by George Lindfield 9780128036365

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Description

Introduction to Nature-Inspired Optimization brings together many of the innovative mathematical methods for non-linear optimization that have their origins in the way various species behave in order to optimize their chances of survival. The book describes each method, examines their strengths and weaknesses, and where appropriate, provides the MATLAB code to give practical insight into the detailed structure of these methods and how they work. Nature-inspired algorithms emulate processes that are found in the natural world, spurring interest for optimization. Lindfield/Penny provide concise coverage to all the major algorithms, including genetic algorithms, artificial bee colony algorithms, ant colony optimization and the cuckoo search algorithm, among others. This book provides a quick reference to practicing engineers, researchers and graduate students who work in the field of optimization.

A succinct introduction to key algorithms in the fast-developing field of nature-inspired algorithms

About the Author
George Lindfield is a former lecturer in Mathematics and Computing at the School of Engineering and Applied Science, Aston University in the United Kingdom. John Penny is an Emeritus Professor of Mechanical Engineering at the School of Engineering and Applied Science, Aston University in the United Kingdom.

Reviews
"Besides being very useful to those who are interested in discrete optimizations problems and applying various nature-inspired metaheuristics to them, the involved reader can also benefit from comparative studies of algorithms highlighting their strengths and weaknesses. The book is written in a clean and easily uderstandable, but still highly scientific language and it is beneficial reading for post-docs and researchers working with MATLAB and interested in metaheuristic approaches to optimization problems." --Zentralblatt MATH



Book Information
ISBN 9780128036365
Author George Lindfield
Format Paperback
Page Count 256
Imprint Academic Press Inc
Publisher Elsevier Science Publishing Co Inc
Weight(grams) 520g

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