Adaptive decision making in ant colony system by reinforcement learning

Authors:
Keiji Kamei;Masumi Ishikawa
Affiliations:
Nishinippon Institute of Technology, Miyakogun, Fukuoka, Japan;Kyushu Institute of Technology, Tobata, Kitakyushu, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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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.