[1]汪佳玲,胡本木,孙越泓.一种基于定位更新技术的人工蜂群聚类算法[J].南京师范大学学报(自然科学版),2015,38(04):95.
　Wang Jialing,Hu Benmu,Sun Yuehong.An Artificial Bee Colony Clustering Algorithm Based onthe Location Update Technology[J].Journal of Nanjing Normal University(Natural Science Edition),2015,38(04):95.
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一种基于定位更新技术的人工蜂群聚类算法()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第38卷
期数:
2015年04期
页码:
95
栏目:
数学
出版日期:
2015-12-30

文章信息/Info

Title:
An Artificial Bee Colony Clustering Algorithm Based onthe Location Update Technology
作者:
汪佳玲; 胡本木; 孙越泓
南京师范大学数学科学学院，江苏 南京 210023
Author(s):
Wang Jialing; Hu Benmu; Sun Yuehong
School of Mathematical Sciences，Nanjing Normal University，Nanjing 210023，China
关键词:
定位更新技术; 人工蜂群算法; 聚类分析; 开采能力
Keywords:
location update technology; artificial bee colony algorithm; clustering analysis; exploitation ability
分类号:
TP391
文献标志码:
A
摘要:
本文提出一种基于定位更新技术的人工蜂群算法，并将其应用于聚类分析问题. 定位更新技术是在每一次待工蜂搜索结束后，充分利用当前最优解和最差解的信息，对最优解做进一步的更新. 实验表明，基于定位更新技术的人工蜂群聚类算法，提高了算法利用先前的解来寻找更好解的开采能力. 该算法与K-means算法、基于粒子群优化的聚类算法以及基于人工蜂群的聚类算法相比，具有更好的聚类性能.
Abstract:
In this paper，an artificial bee colony（ABC）algorithm based on location update technology is proposed and applied to the problems of clustering analysis. The technology makes the algorithm fully use the information of current optimal solution and the worst solution to do further location update of current optimal solution after the search of onlookers. Experiments show that the ABC algorithm based on location update technology enhances the exploitation ability of applying the previous solutions to look for better solutions. The proposed algorithm also has better clustering performance compared with K-means algorithm，clustering algorithms based on particle swarm optimization and artificial bee colony.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2014-06-13.
基金项目：教育部人文社会科学研究青年基金（12YJCZH179）、国家自然科学基金项目（11371197）.
通讯联系人：孙越泓，博士，副教授，研究方向：智能优化及图像处理，E-mail：05234@njnu.edu.cn
更新日期/Last Update: 2015-12-30