Particle swarm optimization with an enhanced learning strategy and crossover operator

作者：

Highlights：

• PSOLC is a new PSO variant with high exploitation and exploration capabilities.

• It comprises new learning and parameter updating schemes, and crossover operator.

• In the learning strategy, each particle learns from the pbests of all particles.

• The self-cognition coefficient is computed based on the quality of the pbests.

• The crossover operator injects randomness to some randomly selected particles.

摘要

•PSOLC is a new PSO variant with high exploitation and exploration capabilities.•It comprises new learning and parameter updating schemes, and crossover operator.•In the learning strategy, each particle learns from the pbests of all particles.•The self-cognition coefficient is computed based on the quality of the pbests.•The crossover operator injects randomness to some randomly selected particles.

论文关键词：Particle swarm optimization,Swarm intelligence,Optimization,Enhanced learning strategy,Parameter updating,Crossover operator

论文评审过程：Received 16 July 2020, Revised 6 January 2021, Accepted 7 January 2021, Available online 11 January 2021, Version of Record 19 January 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.106768