ZHANG W, ZHANG R Y,et al.Multi-group cooperation particle swarm optimization algorithm based on elite knowledge guidance[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(6):116-128.

doi:10.16186/j.cnki.1673-9787.2022070038

Received:2022/07/17

Revised:2022/12/29

Published:2024-09-24

Multi-group cooperation particle swarm optimization algorithm based on elite knowledge guidance

ZHANG Wei, ZHANG Runyu

School of Electrical Engineering and Automation， Henan Polytechnic University，Jiaozuo 454000，Henan，China

Abstract: Objectives To overcome premature convergence and improve the speed and accuracy of PSO， Methods a multi-group cooperation particle swarm optimization algorithm（MGCPSO） based on elite knowledge guidance was proposed.Firstly，the logistic mapping based on the power function constraint was used for obtaining a uniform initial distribution， which could speed up and improve the probability of finding the optimal solution.Secondly，multi-populations were dynamically divided during the algorithm execution phase and the elite knowledge was utilized for guiding the inferior particles，that could effectively realize information sharing and reduce the exploration blindness of particles.Finally，the mutation operation was carried out by combining the opposition-based learning and the extreme perturbation strategy with elite knowledge， which could help the particles expand their search area and strengthen the fine searching within the optimal neighborhood. Results  In order to verify the performance of MGCPSO，simulation experiments were conducted on 30-dimension and 100-dimension test functions.Simulation results showed that MGCPSO performed well in both convergence speed and convergence accuracy compared with other improved algorithms. Conclusions  The multi-group cooperative optimization could effectively avoid the problem of premature convergence and falling into local optimum，and could also improve the global search ability and local exploitation ability.

Key words:particle swarm optimization algorithm;logistic mapping;multi-group;elite knowledge;opposition-based learning;extreme disturbance