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International Conference on Neural Information Processing

ICONIP 2010: Neural Information Processing. Theory and Algorithms pp 609–617Cite as

Adaptive Decision Making in Ant Colony System by Reinforcement Learning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6443))

Abstract

Ant Colony System is a viable method for routing problems such as TSP, because it provides a dynamic parallel discrete search algorithm. Ants in the conventional ACS are unable to learn as they are. In the present paper, we propose to combine ACS with reinforcement learning to make decision adaptively. We succeeded in making decision adaptively in the Ant Colony system and in improving the performance of exploration.

In 2007 he obtained his PhD at the Department of Brain Science and Engineering, Kyushu Institute of Technology. Since 2007, he has been a lecturer, Nishinippon Institute of Technology.

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References

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Author information

Authors and Affiliations

  1. Nishinippon Institute of Technology, 1-11 Aratsu Kanda, Miyakogun, Fukuoka, 800-0394, Japan

    Keiji Kamei

  2. Kyushu Institute of Technology, 1-1 Sensui, Tobata, Kitakyushu, 804-8550, Japan

    Masumi Ishikawa

Authors
  1. Keiji Kamei
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  2. Masumi Ishikawa
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Editor information

Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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© 2010 Springer-Verlag Berlin Heidelberg

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Kamei, K., Ishikawa, M. (2010). Adaptive Decision Making in Ant Colony System by Reinforcement Learning. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_74

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  • DOI: https://doi.org/10.1007/978-3-642-17537-4_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17536-7

  • Online ISBN: 978-3-642-17537-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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