Intelligent task allocation method based on improved QPSO in multi-agent system

被引：14
作者：
Zhang, Feng [1 ]
机构：
[1] Zhejiang Business Coll, Hangzhou 310018, Zhejiang, Peoples R China
来源：
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING | 2020年 / 11卷 / 02期
关键词：
Multi-Agent system; Task allocation; Improved quantum particle swarm optimization; Population diversity; Load balancing;
D O I：
10.1007/s12652-019-01242-0
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To improve the task execution efficiency of multi-Agent system (MAS), an intelligent task allocation method based on improved quantum particle swarm optimization (QPSO) algorithm is proposed. Firstly, the task allocation of MAS system is modeled, and the objective function is constructed by considering the ability and load of Agent. Then, the traditional QPSO algorithm is improved by incorporating chaotic mapping, Gaussian distribution mutation operator and dynamic inertia weighting technology to enhance the diversity of the population and make it have stronger search ability. Finally, the improved QPSO algorithm is used to optimize the task allocation model and get the best allocation scheme. Simulation results show that this method can shorten the task completion time and balance the system load.
引用
收藏
页码：655 / 662
页数：8
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