Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

被引:93
作者
Zhang, Sen [1 ]
Zhou, Yongquan [1 ,2 ]
机构
[1] Guangxi Univ Nationalities, Coll Informat Sci & Engn, Nanning 530006, Peoples R China
[2] Guangxi High Sch, Key Lab Complex Syst & Computat Intelligence, Nanning 530006, Peoples R China
基金
中国国家自然科学基金;
关键词
K-MEANS; ALGORITHM; SEARCH; EVOLUTIONARY;
D O I
10.1155/2015/481360
中图分类号
O1 [数学];
学科分类号
070101 [基础数学];
摘要
One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.
引用
收藏
页数:17
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