• DocumentCode
    418999
  • Title

    Optimization algorithm using multi-agents and reinforcement learning

  • Author

    Kobayashi, Yoko ; Aiyoshi, Eitaro

  • Author_Institution
    Nucl. Eng. Dept., TEPCO Syst. Corp., Tokyo, Japan
  • Volume
    1
  • fYear
    2004
  • fDate
    19-23 June 2004
  • Firstpage
    63
  • 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.
  • Keywords
    combinatorial mathematics; genetic algorithms; learning (artificial intelligence); multi-agent systems; travelling salesman problems; combinatorial optimization; genetic algorithm; multiagents algorithm; optimization algorithm; permutation type; reinforcement learning; traveling salesman problem; Convergence; Distributed computing; Genetic algorithms; Genetic mutations; Learning; Search methods; Testing; Traveling salesman problems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2004. CEC2004. Congress on
  • Print_ISBN
    0-7803-8515-2
  • Type

    conf

  • DOI
    10.1109/CEC.2004.1330838
  • Filename
    1330838
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=418999