A dynamic neighborhood balancing-based multi-objective particle swarm optimization for multi-modal problems

作者：

Highlights：

• The purpose of this study is to solve multi-modal multi-objective problems.

• Using the adaptive parameter adjustment strategy to extend the search space.

• The dynamic neighborhood forming strategy can exchange the information between particles in time.

• The mutation operator is embedded to make the particle jump out of the local optimum.

摘要

•The purpose of this study is to solve multi-modal multi-objective problems.•Using the adaptive parameter adjustment strategy to extend the search space.•The dynamic neighborhood forming strategy can exchange the information between particles in time.•The mutation operator is embedded to make the particle jump out of the local optimum.

论文关键词：Multi-modal multi-objective problem,Particle swarm optimization,Dynamic neighborhood,Mutation operator,Adaptive parameters

论文评审过程：Received 6 May 2020, Revised 30 May 2022, Accepted 30 May 2022, Available online 2 June 2022, Version of Record 4 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117713