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2010 | OriginalPaper | Chapter

Adaptive Decision Making in Ant Colony System by Reinforcement Learning

Authors : Keiji Kamei, Masumi Ishikawa

Published in: Neural Information Processing. Theory and Algorithms

Publisher: Springer Berlin Heidelberg

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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.

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Metadata
Title
Adaptive Decision Making in Ant Colony System by Reinforcement Learning
Authors
Keiji Kamei
Masumi Ishikawa
Copyright Year
2010
Publisher
Springer Berlin Heidelberg
Book
Neural Information Processing. Theory and Algorithms

Print ISBN: 978-3-642-17536-7

Electronic ISBN: 978-3-642-17537-4

Copyright Year: 2010

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

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