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

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 s...

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Bibliographic Details
Published inDiscrete Dynamics in Nature and Society Vol. 2015; no. 2015; pp. 681 - 697
Main Authors Zhang, Sen, Zhou, Yongquan
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Limiteds 01.01.2015
Hindawi Publishing Corporation
Hindawi Limited
Wiley
Subjects
Algorithms
Benchmarking
Cluster analysis
Clustering
Evolutionary algorithms
Hierarchies
Leadership
Local optimization
Mathematical research
Optimization
Optimization theory
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Summary: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.
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ISSN:1026-0226
1607-887X
DOI:10.1155/2015/481360