Particle Swarm Optimization (PSO) algorithm easily falls into local optimal solution when solving complex multimodal function optimization problem.Researches show that dynamic topology and variable parameters can improve the diversity of swarm to improve the situation.However

the effect of topology and parameters is rarely considered simultaneously.In this paper

a new PSO algorithm based on clustering is proposed.It takes

K

-means clustering method to divide the swarm into different neighborhoods dynamically.These neighborhoods have different number of particles and are heterogeneous clusters.A Ring-structure is applied to exchange information among clusters.Furthermore

a novel discriminating method is proposed to detect the ex

ploring stage of a cluster.Each particle adjusts its parameters automatically according to the exploring stage of its cluster.The results of experiments show that the operations above can improve diversity and energetic of the particles

increase exploring ability and convergence

and reduce the dependence of initial election of parameters.