摘要
针对粒子群算法后期收敛速度慢、易陷入局部极值的缺点,提出一种基于粒密度和最大距离积法的简化粒子群聚类算法。通过采用线性递减与随机分布相结合的惯性权重策略、添加极值扰动算子、优化粒子个体最优位置,使粒子群算法能够快速收敛于全局最优。再把改进后的粒子群算法与K-means算法相结合,解决Kmeans算法因随机初始聚类中心而导致聚类效果差、不稳定等问题。通过实验分析,该算法的聚类结果准确率更高、收敛速度更快、稳定性更强。
After analyzing the disadvantages of slow convergence in the late process and local extreme of particle swarm optimization,this paper proposed a simplified PSO clustering algorithm based on granules and maximum distances product method.By adopting tactics of linearly decreasing weight and random distribution,adding the extremum disturbance operator,optimizing the individual extremum of particles,the PSO algorithm could quickly converge to global optimal. Then the improved PSO algorithm combined with K-means algorithm,to solve poor clustering effect and instability of K-means algorithm caused by random initial clustering center. Experiment results show that this algorithm has higher accuracy,faster convergence rate and stronger stability.
出处
《计算机应用研究》
CSCD
北大核心
2014年第12期3550-3552,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(11171095
71371065)
湖南省科技计划资助项目(2013SK3146)
湖南省自然科学衡阳联合基金资助项目(10JJ8008)
关键词
简化粒子群算法
粒密度
最大距离积法
随机分布
极值扰动算子
K-MEANS算法
simplified particle swarm optimization
granule density
maximum distances product
random distribution
disturbed extremum
K-means algorithm
