一种蜜蜂交配优化聚类算法  

A Honey-Bee Mating Optimization Clustering Algorithm

作　　者：罗可[1] 李莲[1] 周博翔[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,湖南长沙410114

出　　处：《电子学报》2014年第12期2435-2441,共7页Acta Electronica Sinica

基　　金：国家自然科学基金(No.11171095;No.71371065);湖南省自然科学衡阳联合基金(No.10JJ8008);湖南省科技计划项目(No.2013SK3146)

摘　　要：K-means算法因简单、高速等特点而被广泛应用,但该算法仍然存在依赖于初始聚类中心、易陷入局部最优等缺陷.为此,提出了一种蜜蜂交配优化聚类算法.该算法利用密度和距离初始化蜂群,并将局部搜索能力较强的粗糙集聚类算法作为工蜂的一种编码,以增强算法的局部搜索能力,最后在迭代过程中不断引入随机种群,增加种群的多样性,提高算法的全局寻优能力.实验结果表明,该算法不仅能有效抑制早熟收敛,而且具有较强的稳定性,较好的聚类效果.K-means algorithm is the most widely used method due to its easy understanding and fast speed .However,this method has the disadvantage that the clustering results depend on the selection of the initial clustering center and it is easy to fall into local optimal .For this reason,this paper proposed a honey-bee mating optimization clustering algorithm .It generates initial swarm by density and distance,and regards rough set clustering algorithm which has strong local search ability as a code of the works to en-hance the local search ability of the algorithm .At last,in order to improve the diversity level of the swarm and the global optimiza-tion ability of the algorithm,random swarm population are introduced continuously in the iterative process .Our experiments show that the proposed algorithm not only can effectively suppress premature convergence,but also has strong stability and produces good clustering results .

关 键 词：聚类 蜜蜂交配优化 粗糙集 K-MEANS 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]