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Multi-agent Co-evolutionary Scheduling Approach based on Genetic Reinforcement Learning
Wei YZ(魏英姿); Jiang, Xinli; Gu KF(谷侃锋)
Department现代装备研究室
Conference Name5th International Conference on Natural Computation, ICNC 2009
Conference DateAugust 14-16, 2009
Conference PlaceTianjian, China
Author of SourceTianjin University of Technology
Source Publication5th International Conference on Natural Computation, ICNC 2009
PublisherIEEE Computer Society
Publication PlacePiscataway, NJ, USA
2009
Pages573-577
Indexed ByEI
EI Accession number20101512839844
Contribution Rank2
ISBN978-0-7695-3736-8
AbstractThe paper presents an adaptive iterative distributed scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. The manufacturing system is scheduled by the multi-agent system where every machine and job is associated with its own software agent. Each agent learns how to select presumably good schedules, by this way the size of the search space can be reduced. In order to get adaptive behavior, genetic algorithm is incorporated to drive parallel search and the evolution direction. Meanwhile, the reinforcement learning system is done with the phased Q-learning by defining the intermediate state pattern. The paper suggests a cooperation technique for the agents, as well. We also analyze the time and the solution and present some experimental results.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19981
Collection智能产线与系统研究室
Corresponding AuthorWei YZ(魏英姿)
Affiliation1.Shenyang Ligong University, Shenyang 110168, China
2.Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
Recommended Citation
GB/T 7714
Wei YZ,Jiang, Xinli,Gu KF. Multi-agent Co-evolutionary Scheduling Approach based on Genetic Reinforcement Learning[C]//Tianjin University of Technology. Piscataway, NJ, USA:IEEE Computer Society,2009:573-577.
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