关键词: 粒子群算法, 差分进化算法, 变异, 交叉

Abstract: This paper proposed a novel Particle Swarm Optimization (PSO) hybrid algorithm to improve the optimum speed and performance of the PSO algorithm. This new algorithm introduced a dynamic proportion operator into differential evolution algorithm and also introduced mutation, crossover operator from DE algorithm into PSO algorithm. Then the position updating formula of PSO was reconstructed. At last, this paper chose four reference functions to have a test and compared the results with other PSO hybrid algorithms. The simulation results verify the effectiveness of this approach.

Key words: PSO algorithm, differential evolution algorithm, mutation, crossover