Introduction To Nature-inspired Optimization

Paperback | August 1, 2017

byGeorge Lindfield, John Penny

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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

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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, pr...

Format:PaperbackDimensions:272 pages, 8.75 × 6.35 × 0.68 inPublished:August 1, 2017Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128036362

ISBN - 13:9780128036365

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Table of Contents

1. Introduction
2. Genetic algorithms (GAs).
3. Artificial bee colony (ABC) algorithm
4. The bat algorithm.
5. Strawberry optimization algorithm
6. Ant colony optimization (ACO)
7. Cuckoo search algorithm
8. Other algorithms and hybrid algorithms
9. General comparison of the nature of the methods