Fitness peak clustering based dynamic multi-swarm particle swarm optimization with enhanced learning strategy

作者：

Highlights：

• A FPC-based dynamic multi swarm PSO with enhanced learning strategy is presented.

• FPC-based clustering method is used to partition population into several sub-swarms.

• Dynamic multi swarm evolutionary strategy is used to balance global and local search.

• Enhanced learning strategy applied in some particles can accelerate convergence.

摘要

•A FPC-based dynamic multi swarm PSO with enhanced learning strategy is presented.•FPC-based clustering method is used to partition population into several sub-swarms.•Dynamic multi swarm evolutionary strategy is used to balance global and local search.•Enhanced learning strategy applied in some particles can accelerate convergence.

论文关键词：Particle swarm optimization,Comprehensive learning,Fitness Peak clustering,Enhanced learning strategy

论文评审过程：Received 31 August 2020, Revised 24 September 2021, Accepted 25 November 2021, Available online 4 December 2021, Version of Record 9 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116301