A new hierarchical multi group particle swarm optimization with different task allocations inspired by holonic multi agent systems
作者：
Highlights：
• Hierarchical multi group PSO is proposed inspired by holonic organization in multi agent systems.
• Proposed structure provides a lot of facilities for improving the performance of PSO.
• For creating a suitable balance between exploration and exploitation, different tasks are assigned to different groups.
• Particles use different parameter settings, dynamic neighborhood topologies and learning strategies based on their group's task.
• Experimental results indicate that HPSO-DTA surpasses other algorithms.
摘要
•Hierarchical multi group PSO is proposed inspired by holonic organization in multi agent systems.•Proposed structure provides a lot of facilities for improving the performance of PSO.•For creating a suitable balance between exploration and exploitation, different tasks are assigned to different groups.•Particles use different parameter settings, dynamic neighborhood topologies and learning strategies based on their group's task.•Experimental results indicate that HPSO-DTA surpasses other algorithms.
论文关键词：Particle swarm optimization,Hierarchical multi group structure,Holonic organization, Multi agent systems,Task allocation,Exploration/Exploitation
论文评审过程：Received 19 October 2018, Revised 22 October 2019, Accepted 5 February 2020, Available online 7 February 2020, Version of Record 9 March 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113292
