Study on the Local Search Ability of Particle Swarm Optimization

Particle swarm optimization (PSO) has been shown to perform well on many optimization problems. However, the PSO algorithm often can not find the global optimum, even for unimodal functions. It is necessary to study the local search ability of PSO. The interval compression method and the probabilistic characteristic of the searching interval of particles are used to analyze the local search ability of PSO in this paper. The conclusion can be obtained that the local search ability of a particle is poor when the component of the global best position lies in between the component of the individual best position and the component of the current position of the particle. In order to improve the local search ability of PSO, a new learning strategy is presented to enhance the probability of exploitation of the global best position. The experimental results show that the modified PSO with the new learning strategy can improve solution accuracy.