A new golden ratio local search based particle swarm optimization

Abstract

At beginning of the search process of particle swarm optimization, one of the disadvantages is that PSO focuses on the global search while the local search is weakened. However, at the end of the search procedure, the PSO focuses on the local search as almost all the particles converge into small areas which could cause the particle swarm to be trapped in the local minima if no particle is found near the minima at the beginning of the search procedure. To improve the optimization performance, the local search is necessary for particle swarm optimization. In this paper, the golden ratio is used to determine the size of the search area. Only two positions need to be checked in order to find whether there are local positions with lower fitness value around a certain particle position. It is also tested using several well-known benchmarks with high dimensions and a large search space for the efficiency of the proposed method.