关键词: 粒子群算法（PSO）, 随机鞭策机制, 散漫度, 寻优精度, 收敛速度

Abstract: The Particle Swarm Optimization（PSO） has problems as being trapped in local minima due to premature convergence and weakness of global search capability. To overcome these disadvantages, Dispersion Particle Swarm Optimization Algorithm based on Random Whip Mechanism（EGPSO） is proposed. Firstly, the concept of particles’ dispersion is presented. In order to avoid falling into local optimum, the algorithm determines the state of the loose particles and marks them by evaluating the dispersion of each particle dynamically, and then uses random whip mechanism to deal with loose particles. Secondly, in order to further improve the algorithm’s convergence speed and accuracy, EGPSO handles active particles by using the optimal location of history. Experimental results on eleven standard benchmark functions demonstrate that EGPSO outperforms original PSO and the other related algorithms in terms of the solution quality and the stability.

Key words: Particle Swarm Optimization（PSO）, random whip mechanism, dispersion, optimization accuracy, convergence speed