基于动态随机搜索和佳点集构造的改进粒子群优化算法  

Improved particle swarm optimization based on dynamic random search technique and good-point set

作　　者：梁昔明[1] 陈富[1] 龙文[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083

出　　处：《计算机应用》2011年第10期2796-2799,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60874070;61074069);高等学校博士点基金资助项目(20070533131);教育部留学回国人员科研启动基金资助项目

摘　　要：针对粒子群优化算法局部搜索能力不足和易出现早熟收敛的问题,提出一种基于动态随机搜索和佳点集构造的改进粒子群优化算法。该算法通过引入动态随机搜索技术,对种群当前最优位置进行局部搜索;采用佳点集构造对陷入早熟收敛的种群重新初始化;引入负梯度方向直线搜索来加速算法寻优。仿真实验结果表明,与标准粒子群优化(SPSO)算法和耗散粒子群优化(DPSO)算法比较,提出的改进算法具有快速的收敛能力而且能有效地跳出局部最优,优化性能得到明显提高。In order to overcome the problems of poor local search and premature convergence on Particle Swarm Optimization（PSO） algorithm,an improved particle swarm optimization approach based on Dynamic Random Search Technique（DRST） and good-point set was proposed in this paper.DRST was introduced to optimize the current best position of the swarm.On the other hand,reinitialization with a good-point set manner was employed for the swarm falling into premature convergence to go out of the local optimum.Linear search in the negative gradient direction was also applied to accelerate the optimization.In the end,an experiment was given and the results show that the improved algorithm has rapid convergence,great ability of preventing premature convergence and better performance than Standard Particle Swarm Optimization（SPSO） and Dissipative Particle Swarm Optimization（DPSO）.

关 键 词：粒子群优化 局部搜索能力 早熟收敛 动态随机搜索技术 佳点集 负梯度 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]