A Random Perturbation Particle Swarm Optimization Algorithm
Quan Xian-zhang · Jisuanji gongcheng · 2006
A novel particle swarm optimization algorithm——random perturbation particle swarm optimization algorithm(RP-PSO) based on independence of population structure is proposed.To retain diversity of population and avoid being plunged to local optimum,it initializes the worst individual in population over again,at the same time,the best previous particle of each individual is randomly perturbed after evolutionary computation every time to improve its running efficiency and precision of over all optimization searching.Test results of complex functions demonstrate RAPSO is superior to basic particle swarm optimization in quality and efficiency.