Title :
Optimization algorithm using multi-agents and reinforcement learning
Author :
Kobayashi, Yoko ; Aiyoshi, Eitaro
Author_Institution :
Nucl. Eng. Dept., TEPCO Syst. Corp., Tokyo, Japan
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;
Conference_Titel :
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN :
0-7803-8515-2
DOI :
10.1109/CEC.2004.1330838
