基于探索与利用平衡理论的灾变粒子群算法

Particle Swarm Optimization with Exhaustive Disturbance Based on Exploration-Exploitation Balance Theory

基于算法只有适应优化问题的特性才能表现出优异性能的观点,在探索与利用平衡的理论框架下将灾变机制引入粒子群算法.在对灾变的强度和范围进行深入研究的基础上,提出4种控制灾变的方法,并通过多组正交实验研究最佳的灾变触发方式.通过实验分析得出如下结论:灾变对高维问题的作用有限;灾变强度控制在15%以下为宜;以种群多样性作为灾变的触发条件,能得到较好效果.以上述结论为基础提出自适应灾变粒子群算法,并通过与其他算法对比验证文中算法具有较好性能.

Based on the viewpoint that the algorithm gain a good performance only because it fits the characters of the optimization problem, exhaustive disturbance mechanism is introduced in the particle swarm algorithm under the theoretical framework of the exploration-exploitation balance. Based on the thorough researches of the intensity and range for exhaustive disturbance, four kinds of method for employing exhaustive disturbance are proposed in this paper. Some groups of orthogonal experiments are designed to find the best way of employing exhaustive disturbance. By analyzing the experimental results, the following conclusions are drawn. Exhaustive disturbance has its limits while dealing with high dimensional optimization problems, the intensity of exhaustive disturbance needs to be restricted within 15%, and the triggering condition of exhaustive disturbance based on population diversity shows better performance than the other triggering conditions. Finally, on the basis of the above conclusions, adaptive particle swarm optimization with exhaustive disturbance is proposed. Comparing with other algorithms, the proposed algorithm has a better performance.