A Hybrid Variants Particle Swarm Optimization Algorithm

摘要

A hybrid variants particle swarm optimization (HVPSO) algorithm is proposed in this paper. HVPSO is a stand particle swarm optimization algorithm including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants to make the convergence faster. Two different HVPSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate that the GLbest-PSO provides better results for all the cross-over variants than that of the cPSO.