关键词: 粒子群算法, 自适应排斥因子, 蒙特卡洛模拟, 多峰问题, 局部最优解

Abstract: As the multimodal complex problem has many local optima, it is difficult for the basic Particle Swarm Optimization (PSO) to effectively solve this kind of problem. To conquer this defect, firstly, Monte Carlo method was used to simulate the fly trajectory of particle, and the reason for falling into local optima was concluded. Then, by defining distance, average distance and maximal distance between particles, an adaptive control factor named Adaptive Rejection Factor (ARF) for controlling local optimum position and global optimum position was proposed to increase the ability for escaping from local optima. In order to test the proposed strategy, three test benchmarks including Rosenbrock, Ackley and Griewank were selected to conduct the analysis of convergence property and statistical property. The 60 times independent runs show that the improved PSO based on ARF (ARFPSO) has the best value of 53.82, 2.1203 and 5.32E-004, which is better than the both contrast algorithms. The results show that ARFPSO can effectively avoid premature phenomenon, and the complexity analysis of the algorithm also shows that the introduced strategy does not increase computational complexity.

Key words: Particle Swarm Optimization (PSO), adaptive rejection factor, Monte Carlo simulation, multimodal problem, local optima