Overview
- Recent research and source of reference of knowledge on nature-inspired algorithms and their applications
- Focuses on the implementation of nature-inspired solutions for optimisation based on empirical studies
Part of the book series: Studies in Computational Intelligence (SCI, volume 193)
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Table of contents (18 chapters)
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Front Matter
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Section I: Introduction
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Section II: Evolutionary Intelligence
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Section IV: Social-Natural Intelligence
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Section V: Multi-Objective Optimisation
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Back Matter
Keywords
About this book
Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume 'Nature-Inspired Algorithms for Optimisation' is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.
Editors and Affiliations
Bibliographic Information
Book Title: Nature-Inspired Algorithms for Optimisation
Editors: Raymond Chiong
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-00267-0
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-642-00266-3Published: 28 April 2009
Softcover ISBN: 978-3-642-10130-4Published: 28 October 2010
eBook ISBN: 978-3-642-00267-0Published: 02 May 2009
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XVIII, 516
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Operations Research/Decision Theory