Dynamic Probabilistic Particle Swarm Optimization Based on Varying Multi-Cluster Structure

Regarding to the characteristic of Gbest model and Lbest model in original particle swarm optimization, a neighborhood topology structure is developed,called multi-cluster structure.Moreover,a varying neighborhood strategy based on multi-cluster is proposed to coordinate exploration and exploitation.Furthermore,the information dissemination of several topologies is analyzed theoretically,and the statistical properties of canonical topologies and varying neighborhood topology are analyzed from graph theory.Gaussian dynamic particle swarm with several canonical topologies and varying topology are tested on five benchmark functions which are commonly used in the evolutionary computation.Experimental simulation results demonstrate that dynamic probabilistic particle swarm optimization with the varying neighborhood topology can solve complex optimization problems and escape from local optimal solutions efficiently.The results also reveal that the proposed method enhances the global search ability obviously.