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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260614" target="_blank" >RIV/61989100:27240/25:10260614 - isvavai.cz</a>

Result on the web

<a href="https://www.techscience.com/cmc/v83n1/60089" target="_blank" >https://www.techscience.com/cmc/v83n1/60089</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2025.060170" target="_blank" >10.32604/cmc.2025.060170</a>

Result language

angličtina

Original language name

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

Original language description

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&apos;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&apos;s robustness and efficiency in solving both optimization and clustering challenges, making it a promising tool for complex data analysis tasks.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20500 - Materials engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

CMC-Computers Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Volume of the periodical

83

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

22

Pages from-to

475-496

UT code for WoS article

001459657000001

EID of the result in the Scopus database

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