A Novel Particle Swarm Optimal Algorithm Hybridized with Random Disturbance Term
Yi Zuo, Yong Chen · Advanced materials research · 2013
Recently, Particle Swarm Optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its high speed computational capability. However, traditional PSO has premature convergence problem. To prevent the premature convergence of PSO, some modified algorithms is proposed, based on these modified algorithms a novel hybrid algorithm with random disturbance is proposed in this paper. The performance of the proposed algorithm is testified on a suite of benchmark functions. The simulation results show that this hybridized PSO has great capability of preventing premature convergence and faster computing speed.