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. © 2010 Springer-Verlag.
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CITATION STYLE
Kamei, K., & Ishikawa, M. (2010). Adaptive decision making in ant colony system by reinforcement learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6443 LNCS, pp. 609–617). https://doi.org/10.1007/978-3-642-17537-4_74
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