[1]朱书伟,周治平,张道文.融合并行混沌萤火虫算法的K-调和均值聚类[J].智能系统学报编辑部,2015,10(6):872-880.[doi:10.11992/tis.201505043]
 ZHU Shuwei,ZHOU Zhiping,ZHANG Daowen.K-harmonic means clustering merged with parallel chaotic firefly algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(6):872-880.[doi:10.11992/tis.201505043]
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年6期
页码:
872-880
栏目:
出版日期:
2015-12-25

文章信息/Info

Title:
K-harmonic means clustering merged with parallel chaotic firefly algorithm
作者:
朱书伟 周治平 张道文
江南大学物联网工程学院, 江苏无锡 214122
Author(s):
ZHU Shuwei ZHOU Zhiping ZHANG Daowen
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
关键词:
K-调和均值局部最优萤火虫算法聚类并行混沌优化混沌局部搜索映射模型种群多样性
Keywords:
K-harmonic meanslocal optimumfirefly algorithmclusteringparallel chaotic optimizationchaotic local searchmap modeldiversity of population
分类号:
TP18
DOI:
10.11992/tis.201505043
摘要:
针对K-调和均值算法易陷于局部最优的缺点,提出一种基于改进萤火虫算法(firefly algorithm, FA)的K-调和均值聚类算法。将基于FA的粗搜索与基于并行混沌优化FA的精细搜索相结合,其中精细搜索部分首先通过FA搜索到当前最优解及次优解,然后通过改进的logistic映射与并行混沌优化策略产生混沌序列在其附近直接搜索,以增强算法的寻优性能。最终,将这种改进的FA用于K-调和均值算法聚类中心的优化。实验结果表明:该算法不但对几种测试函数具有更高的搜索精度,而且对6种数据集的聚类结果均有一定的改善,有效地抑制了K-调和均值算法陷于局部最优的问题,提高了聚类准确性和稳定性。
Abstract:
The K-harmonic means algorithm(KHM) has the disadvantage of easily falling into a local optimum. To solve this problem, we propose a hybrid KHM based on an improved firefly algorithm(FA). In this paper, we combined raw FA-based searching with parallel chaotic FA-based elaborate searching. In the elaborate searching, we found the current best and second-best solutions using the FA, then we used an improved logistic map model combined with parallel chaotic optimization to search this area in order to enhance the searching ability of the algorithm. Finally, we used the improved FA to optimize the cluster centers obtained by the KHM. Experimental results demonstrate that the proposed algorithm not only had higher search precision for several test functions, but also improved the clustering accuracy and stability of six datasets, effectively avoiding being trapped into a local optimum.

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备注/Memo

备注/Memo:
收稿日期:2015-05-27;改回日期:。
基金项目:江苏省产学研联合创新资金-前瞻性联合研究基金资助项目(BY2013015-33).
作者简介:朱书伟,男,1990年生,硕士研究生,主要研究方向为人工智能与模式识别。周治平,男,1962年生,教授,博士,主要研究方向为智能检测、自动化装置、网络安全等。张道文,男,1989年生,硕士研究生,主要研究方向为数据挖掘与人工智能。
通讯作者:朱书伟.E-mail:6131905056@vip.jiangnan.edu.cn.
更新日期/Last Update: 1900-01-01