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Parallel Ant Colony Optimizer Based on Adaptive Resonance Theory Maps

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5506))

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Abstract

This paper studies a parallel ant colony optimizer and its application to the traveling sales person problems. The parallel processing is based on the adaptive resonance theory map that divide the input space into subspaces. The ants are classified into two types: local ant for local search within either subspace and global ant for search of whole input space. Communication between local and global ants is a key for effective parallel processing. Applying the algorithm to basic bench marks, we can suggest that our algorithm realize fast and reasonable search.

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Author information

Authors and Affiliations

  1. Hosei University, Koganei, Tokyo, 184-0002, Japan

    Hiroshi Koshimizu & Toshimichi Saito

Authors
  1. Hiroshi Koshimizu
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  2. Toshimichi Saito
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Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Network Design and Research Center, 680-4 Fukuoka, 820-8502, Kawazu, Iizuka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Koshimizu, H., Saito, T. (2009). Parallel Ant Colony Optimizer Based on Adaptive Resonance Theory Maps. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_139

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  • DOI: https://doi.org/10.1007/978-3-642-02490-0_139

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02489-4

  • Online ISBN: 978-3-642-02490-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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