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

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  • Sen Zhang
  • Yongquan Zhou

Abstract

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.

Suggested Citation

  • Sen Zhang & Yongquan Zhou, 2015. "Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-17, November.
    • Handle: RePEc:hin:jnddns:481360
    DOI: 10.1155/2015/481360
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    Cited by:

    1. Wee How Khoh & Ying Han Pang & Shih Yin Ooi & Lillian-Yee-Kiaw Wang & Quan Wei Poh, 2023. "Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning," Sustainability, MDPI, vol. 15(11), pages 1-21, May.
    2. Salil Madhav Dubey & Hari Mohan Dubey & Surender Reddy Salkuti, 2022. "Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems," Energies, MDPI, vol. 15(15), pages 1-29, August.

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