一种基于蚁群算法的K-means算法——在公路运输枢纽宏观布局规划中的应用  

Application of K-means algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm

作　　者：孟岩[1] 刘希玉[1] 刘艳丽[1] 

机构地区：[1]山东师范大学管理与经济学院,济南250014

出　　处：《计算机工程与应用》2008年第1期179-182,共4页Computer Engineering and Applications

基　　金：山东省自然科学基金(the Natural Science Foundation of Shandong Province of China under Grant No.Z2004G02);泰山学者建设工程专项经费资助项目;山东省教育厅资助科研课题(the Research Project of Department of Education of Shandong Province;China under Grant No.J05G01)。

摘　　要：公路运输的发展有效促进了我国经济持续、快速的发展,但公路建设和运输枢纽建设呈现出不平衡性。因此需采用聚类分析对公路主枢纽城市进行聚类,划分层次来进行功能分析。K-means算法是聚类分析中使用最为广泛的算法之一,但算法具有初始中心点和聚类个数不确定等方面的缺点。针对其缺点,提出将基于蚁群算法的K-means算法应用于在公路运输枢纽布局规划中。实验结果表明,与单独使用两种算法相比,该算法更能有效地解决公路主枢纽城市的聚类问题。Development of highway transportation promotes sustainable and rapid development in economy of our country effectively.But construction of highway and transportation hub shows the nature of imbalance.So highway main hub cities must be clustered using cluster analysis,and then divided level in order to functional analyze.K-means algorithm is the most widely used algorithm in clustering analysis,which clustering numbers and initial clustering center are uncertain.This paper proposes application of K-means algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm.The experimental resuits show this algorithm can more effectively solve clustering problem than K-means algorithm and ant clustering algorithm.

关 键 词：K—means算法 蚁群聚类算法 公路运输 主枢纽城市 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]