关键词: 粒子群优化（PSO）, 多策略, 差分变异, 扰动策略, 数值优化

Abstract: Most particle swarm optimization (PSO) algorithms use a single learning pattern for all particles, which does not benefit to save the heuristic information. This paper introduces a modified differential mutation strategy to update the velocity of particle in order to diversify the PSO population. Then this paper proposes a multiple strategies interactive learning mechanism based PSO (DPPSO), which combines differential mutation with the perturbed particle updating strategy as the updating pattern. Each particle generates two intermediate particles from two strategies and selects a better one as its new position. Therefore, each particle can dynamically select its biased generation strategy to diversify the particle population and the searching trajectories. The particles can obtain more beneficial heuristic information, which guides particles to the promising search area. The experimental results indicate the effectiveness and the reciprocal reinforcement of two strategies. Compared with other PSO variants, DPPSO has the best comprehensive performance, with powerful global exploration and fine locating abilities.

Key words: particle swarm optimization (PSO), multiple strategies, differential mutation, perturbation strategy, numerical optimization