New Two-subpopulation Particle Swarm Optimization Algorithm

A new two-subpopulation Particle Swarm Optimization(PSO) algorithm is proposed.The information of search space is taken full advantage in the algorithm.The search rang is extended through main subpopulation particle swarm and assistant subpopulation particle swarm,which search direction are inversed completely.Without increasing the size of particle swarm,the optimal precision of high dimension functions is improved and the risk of trapping into local optima is decreased effectively.The efficiency of the algorithm is verified by the simulation results of three benchmark functions and the comparison with two classical PSO algorithms.