© 2006

Swarm Intelligent Systems

  • Nadia Nedjah
  • Luiza de Macedo Mourelle

Part of the Studies in Computational Intelligence book series (SCI, volume 26)

Table of contents

  1. Front Matter
    Pages I-XX
  2. Methodologies Based on Particle Swarm Intelligence

    1. Front Matter
      Pages 1-1
    2. Ajith Abraham, He Guo, Hongbo Liu
      Pages 3-25
    3. Tim Hendtlass
      Pages 27-58
    4. Michael O'Neill, Finbar Leahy, Anthony Brabazon
      Pages 59-74
    5. Derek Messie, Jae C. Oh
      Pages 75-90
  3. Experiences Using Particle Swarm Intelligence

    1. Front Matter
      Pages 92-92
    2. Yaniv Altshuler, Vladimir Yanovsky, Israel A. Wagner, Alfred M. Bruckstein
      Pages 93-132
    3. Arun Khosla, Shakti Kumar, K. K. Aggarwal, Jagatpreet Singh
      Pages 149-173
    4. Arun Khosla, Shakti Kumar, K. K. Aggarwal, Jagatpreet Singh
      Pages 175-184
  4. Back Matter
    Pages 185-185

About this book


This volume offers a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems as well as applications and interesting experiences using the particle swarm optimisation.

Swarm intelligence is an innovative computational way to solve hard problems  which is at the heart of computational intelligence. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a  good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in  multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position  that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions.

Instead of designing complex and centralized systems, nowadays designers rather prefer to work with many small and autonomous agents. Each agent may prescribe to a global strategy. An agent acts on the simplest of rules. The many agents co-operating within the system can solve very complex problems with a minimal design effort. In General, multi-agent systems that use some swarm intelligence are said to be swarm intelligent systems. They are mostly used as search engines and optimization tools. The book should be useful both for beginners and experienced researchers in the field of computational intelligence.


MATLAB agents algorithm algorithms behavior computational intelligence fuzzy intelligence intelligent systems multi-agent system multi-agent systems optimization particle swarm particle swarm algorithm swarm intelligence

Editors and affiliations

  • Nadia Nedjah
    • 1
  • Luiza de Macedo Mourelle
    • 2
  1. 1.Department of Electronics Engineering and Telecommunications - DETEL, Faculty of Engineering - FENState University of Rio de Janeiro - UERJMaracanãBrazil
  2. 2.Department of Electronics Engineering and Telecommunications - DESC, Faculty of Engineering - FENState University of Rio de Janeiro - UERJMaracanãBrazil

Bibliographic information

  • Book Title Swarm Intelligent Systems
  • Authors Nadia Nedjah
    Luiza Macedo Mourelle
  • Series Title Studies in Computational Intelligence
  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-540-33868-0
  • Softcover ISBN 978-3-642-07041-9
  • eBook ISBN 978-3-540-33869-7
  • Series ISSN 1860-949X
  • Series E-ISSN 1860-9503
  • Edition Number 1
  • Number of Pages XX, 184
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Mathematical and Computational Engineering
    Artificial Intelligence
  • Buy this book on publisher's site
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