A Stochastic Perturbing Particle Swarm Optimization Model

The particle swarm optimization (PSO) algorithmis a generally used optimal algorithm, which exhibits good performance on optimization problems in complex search spaces. However, traditional PSO model suffers from a local minima, and lacks of effective mechanism to escape from it. This is harmful to its overall performance. This paper presents an improved PSO model called the stochastic perturbing PSO(SPPSO), which tries to overcome such premature convergence through perturbing the swarm with the perturbation and acceptance probability. The performance of the SPPSO is compared with the basic PSO (bPSO) on a set of benchmark functions. Experimental results show that, the new model not only effectively prevent the premature convergence, but also keep the rapid convergence rate like the bPSO.