A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

Li Zhipeng; Wei Xiumei; Jiang XuesongPang Yewen

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A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

机译：A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

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摘要

It is difficult to coordinate the various processes in the process industry. We built a multiagent distributed hierarchical intelligent control model for manufacturing systems integrating multiple production units based on multiagent system technology. The model organically combines multiple intelligent agent modules and physical entities to form an intelligent control system with certain functions. Thmodel consists of system management agent, workshop control agent, and equipment agent. For the task assignment problem with this model, we combine reinforcement learning to improve the genetic algorithm for multiagent task scheduling and use the standard task scheduling dataset in OR-Library for simulation experiment analysis. Experimental results show that the algorithm is superior.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2021年第2期|1796296.1-1796296.12|共12页
作者
Li Zhipeng; Wei Xiumei; Jiang XuesongPang Yewen;
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作者单位

Tongji Univ, Sch Elect & Informat Engn, Inst Machine Learning & Syst Biol, Shanghai 201804, Peoples R China;

Qilu Univ Technol, Sch Comp Sci & Technol, Shandong Acad Sci, Jinan 250353, Peoples R China;

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A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

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