Multi-group cooperation particle swarm optimization algorithm based on elite knowledge guidance
ZHANG Wei
ZHANG Runyu
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 find-ing the optimal solution.Secondly,multi-populations were dynamically divided during the algorithm execu-tion 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 con-ducted on 30-dimension and 100-dimension test functions.Simulation results showed that MGCPSO per-formed 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 con-vergence and falling into local optimum,and could also improve the global search ability and local exploita-tion ability.
Keywords：particle swarm optimization algorithmlogistic mappingmulti-groupelite knowledgeopposition-based learningextreme disturbance
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：13( 116-128 )