A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments

作者：

Highlights：

• A collaborative mechanism is proposed to improve particle swarm optimization.

• A worst-replacement scheme is proposed to update particles’ positions.

• The trajectory of the best particle during optimizing is stored to an external archive.

• The stored solutions estimate a promising optimal region in a new environment.

• The proposed algorithm shows a competitive power in dynamic optimization problems.

摘要

•A collaborative mechanism is proposed to improve particle swarm optimization.•A worst-replacement scheme is proposed to update particles’ positions.•The trajectory of the best particle during optimizing is stored to an external archive.•The stored solutions estimate a promising optimal region in a new environment.•The proposed algorithm shows a competitive power in dynamic optimization problems.

论文关键词：Intelligent systems,Dynamic optimization,Particle swarm optimizer,Collaborative mechanism,History-guided estimation

论文评审过程：Received 29 January 2018, Revised 10 November 2018, Accepted 11 November 2018, Available online 12 November 2018, Version of Record 16 November 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.11.020