Open Access

ARTICLE

Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

Jeng-Shyang Pan^1,2,3, Mengfei Zhang¹, Shu-Chuan Chu^2,*, Xingsi Xue⁴, Václav Snášel⁵

1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
2 School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China
3 Department of Information Management, Chaoyang University of Technology, Taichung, 41349, Taiwan
4 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, China
5 Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava, 70833, Czech Republic

* Corresponding Author: Shu-Chuan Chu. Email:

(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua 2025, 83(1), 475-496. https://doi.org/10.32604/cmc.2025.060170

Received 25 October 2024; Accepted 27 December 2024; Issue published 26 March 2025

Abstract

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis. Traditional clustering algorithms, such as K-means, are widely used due to their simplicity and efficiency. This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm (SPPE) to improve clustering performance. The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution (PPE) algorithm. Firstly, a Variable Neighborhood Search (VNS) factor is incorporated to strengthen the local search capability and foster population diversity. Secondly, a position update model, incorporating a spiral mechanism, is designed to improve the algorithm’s global exploration and convergence speed. Finally, a dynamic balancing factor, guided by fitness values, adjusts the search process to balance exploration and exploitation effectively. The performance of SPPE is first validated on CEC2013 benchmark functions, where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms. To further verify its practical applicability, SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets. Experimental results show that SPPE-K-means improves clustering accuracy, reduces dependency on initialization, and outperforms other clustering approaches. This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges, making it a promising tool for complex data analysis tasks.

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Keywords

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