On Multi-population Parallel Particle Swarm Optimization Algorithm

To improve performance of particle swarm optimization (PSO) algorithm and avoid trapping to local minima, a multi-population parallel particle swarm optimization (DPPSO) algorithm is proposed. In the algorithm, sub populations are divided into exploration and exploitation types. The global version PSO is used in the exploration population to enhance ability of exploring the best individual, and the local version PSO is used in the exploitation population to enhance ability of local search and find the best global result in the local range. Simultaneously, keep communication with sub populations in running. The experimental results show that the restraining premature convergence is enhanced for maintaining the individual diversity.