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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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
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