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.

Applies concepts in nature and biology to develop new algorithms for nonlinear optimization
Offers working MATLAB� programs for the major algorithms described, applying them to a range of problems
Provides useful comparative studies of the algorithms, highlighting their strengths and weaknesses
Discusses the current state-of-the-field and indicates possible areas of future development

About the Authors

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.

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