Multiple Learning Strategies and a Modified Dynamic Multiswarm Particle Swarm Optimization Algorithm with a Master Slave Structure

Abstract

It is a challenge for the particle swarm optimization algorithm to effectively control population diversity and select and design efficient learning models. To aid in this process, in this paper, we propose multiple learning strategies and a modified dynamic multiswarm particle swarm optimization with a master slave structure (MLDMS-PSO). First, a dynamic multiswarm strategy with a master–slave structure and a swarm reduction strategy was introduced to dynamically update the subswarm so that the population could maintain better diversity and more exploration abilities in the early stage and achieve better exploitation abilities in the later stage of the evolution. Second, three different particle updating strategies including a modified comprehensive learning (MCL) strategy, a united learning (UL) strategy, and a local dimension learning (LDL) strategy were introduced. The different learning strategies captured different swarm information and the three learning strategies cooperated with each other to obtain more abundant population information to help the particles effectively evolve. Finally, a multiple learning model selection mechanism with reward and punishment factors was designed to manage the three learning strategies so that the particles could select more advantageous evolutionary strategies for different fitness landscapes and improve their evolutionary efficiency. In addition, the results of the comparison between MLDMS-PSO and the other nine excellent PSOs on the CEC2017 test suite showed that MLDMS-PSO achieved an excellent performance on different types of functions, contributing to a higher accuracy and a better performance.