Optimization algorithm using multi-agents and reinforcement learning

Yoko Kobayashi, Eitaro Aiyoshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper deals with combinatorial optimization of permutation type using multi-agents algorithm (MAA). In order to improve optimization capability, we introduced the reinforcement learning and several processes into this MAA. Optimization capability of this algorithm was compared in traveling salesman problem and it provided better optimization results than the conventional MAA and genetic algorithm.

Original languageEnglish
Title of host publicationProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Pages63-68
Number of pages6
Publication statusPublished - 2004 Sep 13
EventProceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States
Duration: 2004 Jun 192004 Jun 23

Publication series

NameProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Volume1

Other

OtherProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
CountryUnited States
CityPortland, OR
Period04/6/1904/6/23

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Kobayashi, Y., & Aiyoshi, E. (2004). Optimization algorithm using multi-agents and reinforcement learning. In Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 (pp. 63-68). (Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004; Vol. 1).