Abstract: Particle swarm optimization(PSO) is a relatively simple structure which runs very quickly,but it is easily fall into local optimum and appears the phenomenon of premature convergence．Aiming at the PSO existing problems,by setting the proportional coefficient control of inertia weight between influence strength,this paper introduced a kind of novel way using the iteration number and particle size of the distance between the dynamic change inertia weight．At the same time,in order to increase the diversity of population,using "hybrid variation" operator,designed a kind of dynamic particle swarm optimization based on hybrid variable,(HV-DPSO) based on reference function of numerical experiment.The experimental results show that compared with the traditional PSO,the new algorithm not only can effectively avoid premature convergence but also has better convergence effect.

Key words: Particle swarm optimization,Dynamic inertial weight,Hybrid variation,premature convergence,Diversity