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Multi-objective Evolutionary Algorithm Based on Target Space Partitioning Method

ISSN号：1672-5220
期刊名称：Journal of Dong Hua University (english Edition)
时间：0
页码：126-131
分类：R[医药卫生]
作者机构：[1]School of Information Science and Engineering, Shandong Normal University, Jinan (250014), China, [2]Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, Jinan (250014), China, [3]School of Information Engineering, Shandong Management University, Jinan (250357), China, [4]Heart Center, Second Hospital Affiliated to Shandong University of Traditional Chinese Medicine, Jinan (250001), China, [5]School of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan (250014), China
相关基金：Supported by the National Natural Science Foundation of China（No.60743010 and No.60970004）; Natural Science Foundation of Shandong Province（No.ZR2012HW052）; E-Government Con-struction Project（No.2150511）
相关项目：动态自适应协同进化的学习机制研究

作者：尚兆霞，刘弘|

关键词：聚类, 萤火虫算法, 模仿的退火的算法, 中国药药方, clustering firefly algorithm simulated annealing algorithm Chinese medicine prescriptions

中文摘要：

这份报纸被瞄准为中国药(厘米) 学习聚类的方法医药盒子。聚类算法的传统的 K 工具在起始的价值的选择上有象结果的依赖那样的缺点，当处理药方时，在本地最佳套住形式厘米医药盒子。因此，一个新聚类方法基于萤火虫算法和模仿的退火的算法的合作被建议。这个算法动态地决定了萤火虫算法的重复并且模仿由健康的算法改变的退火的采样，并且通过范围的扩大增加了群的差异突然跳，有效地从而避免早熟的问题。从为厘米医药盒子的确定的实验的结果建议了那，与聚类算法的传统的 K 工具作比较，这个方法极大地在单个差异被改进，获得的聚类结果，从这个方法的计算结果在厘米药方上为簇分析有某个参考书价值。

英文摘要：

This paper is aimed to study the clustering method for Chinese medicine（CM） medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.

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