A Particle Swarm Optimization Algorithm Based on the Pattern Search Method

摘要

For the purpose of overcoming the premature property and low execution efficiency of the Particle Swarm Optimization (PSO) algorithm, this paper presents a particle swarm optimization algorithm based on the pattern search. In this algorithm, personal and global optimum particles are chosen in every iteration by a probability. Then, local optimization will be performed by the pattern search and then the original individuals will be replaced. The strong local search function of the pattern search provides an effective mechanism for the PSO algorithm to escape from the local optimum, which avoids prematurity of the algorithm. Simulation shows that this algorithm features a stronger function of global search than conventional PSO, so that the optimization process can be improved remarkably.