Particle swarm optimization algorithm based on two swarm evolution

A new particle swarm optimization algorithm that based on two swarm´s evolution is proposed. In one swarm the linear decreasing weight is used, in the other swarm the random inertia weight is adopted. The random disturbance is added to the formula of position update. During the running time, a new swarm is generated by the contest of two swarm´s evolution. The ability of particle swarm optimization algorithm to break away from the local optimum is improved greatly. The experiment results show that the new algorithm can greatly improve the global convergence ability and enhance the rate of convergence.