Improved quantum particle swarm optimization based on good-point set

In order to solve the problems of premature convergence and poor local search on particle swarm optimization(PSO) algorithm,an improved quantum particle swarm optimization(IQPSO) approach was proposed.Based on quantum particle swarm optimization algorithm(QPSO),good-point set was introduced to the approach to initialize initial angle of quantum position,to improve ergodicity of initial population.To make particle jump out of local extreme value point,the chaotic time series numbers were used to update particle velocity.To prevent particle from premature convergence,mutation process was added in the approach.The simulation experiment results show that the improved algorithm has rapid convergence,good stability and it gives better performance than standard particle swarm optimization(SPSO) and quantum particle swarm optimization(QPSO).