An improved competitive particle swarm optimization for many-objective optimization problems

作者：

Highlights：

• An improved muti-step initialization mechanism is presented.

• Different decision variables adopted different operators to optimize particles.

• A competition based learning strategy for updating swarm particles is suggested.

• Experimental results show it achieved balance between convergence and diversity.

摘要

•An improved muti-step initialization mechanism is presented.•Different decision variables adopted different operators to optimize particles.•A competition based learning strategy for updating swarm particles is suggested.•Experimental results show it achieved balance between convergence and diversity.

论文关键词：Many-objective optimization problems,Many-objective particle swarm optimization,Initialization mechanism,Competitive strategy

论文评审过程：Received 6 November 2020, Revised 17 September 2021, Accepted 16 October 2021, Available online 26 October 2021, Version of Record 6 November 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116118