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

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

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 intelli...

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Main Authors: Zhipeng Li, Xiumei Wei, Xuesong Jiang, Yewen Pang
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2021/1796296
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spelling doaj-b56665d1df374c0c8b793a29536e5da22021-02-15T12:53:07ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472021-01-01202110.1155/2021/17962961796296A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task SchedulingZhipeng Li0Xiumei Wei1Xuesong Jiang2Yewen Pang3Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaIt 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. The model 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.http://dx.doi.org/10.1155/2021/1796296
collection DOAJ
language English
format Article
sources DOAJ
author Zhipeng Li
Xiumei Wei
Xuesong Jiang
Yewen Pang
spellingShingle Zhipeng Li
Xiumei Wei
Xuesong Jiang
Yewen Pang
A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling
Mathematical Problems in Engineering
author_facet Zhipeng Li
Xiumei Wei
Xuesong Jiang
Yewen Pang
author_sort Zhipeng Li
title A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling
title_short A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling
title_full A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling
title_fullStr A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling
title_full_unstemmed A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling
title_sort kind of reinforcement learning to improve genetic algorithm for multiagent task scheduling
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2021-01-01
description 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. The model 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.
url http://dx.doi.org/10.1155/2021/1796296
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AT xuesongjiang akindofreinforcementlearningtoimprovegeneticalgorithmformultiagenttaskscheduling
AT yewenpang akindofreinforcementlearningtoimprovegeneticalgorithmformultiagenttaskscheduling
AT zhipengli kindofreinforcementlearningtoimprovegeneticalgorithmformultiagenttaskscheduling
AT xiumeiwei kindofreinforcementlearningtoimprovegeneticalgorithmformultiagenttaskscheduling
AT xuesongjiang kindofreinforcementlearningtoimprovegeneticalgorithmformultiagenttaskscheduling
AT yewenpang kindofreinforcementlearningtoimprovegeneticalgorithmformultiagenttaskscheduling
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