Nature-Inspired Algorithms for Optimisation

  • Raymond Chiong

Part of the Studies in Computational Intelligence book series (SCI, volume 193)

Table of contents

  1. Front Matter
  2. Section I: Introduction

    1. Thomas Weise, Michael Zapf, Raymond Chiong, Antonio J. Nebro
      Pages 1-50
    2. Kent C. B. Steer, Andrew Wirth, Saman K. Halgamuge
      Pages 51-76
  3. Section II: Evolutionary Intelligence

  4. Section III: Collective Intelligence

    1. Carmelo J. A. Bastos Filho, Fernando B. de Lima Neto, Anthony J. C. C. Lins, Antônio I. S. Nascimento, Marília P. Lima
      Pages 261-277
    2. Ying Tan, Junqi Zhang
      Pages 279-298
    3. Efrén Mezura-Montes, Jorge Isacc Flores-Mendoza
      Pages 299-332
    4. Pablo Rabanal, Ismael Rodríguez, Fernando Rubio
      Pages 333-368
  5. Section IV: Social-Natural Intelligence

    1. Antonio Neme, Sergio Hernández
      Pages 369-387
    2. Heder S. Bernardino, Helio J. C. Barbosa
      Pages 389-411
  6. Section V: Multi-Objective Optimisation

  7. Back Matter

About this book

Introduction

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.

Keywords

algorithm algorithms artificial intelligence collective intelligence complexity computational intelligence evolution evolutionary algorithm genetic algorithms global optimization intelligence learning model optimization proving

Editors and affiliations

  • Raymond Chiong
    • 1
  1. 1.Swinburne University of TechnologyKuching, SarawakMalaysia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-00267-0
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-00266-3
  • Online ISBN 978-3-642-00267-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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