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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20500 - Materials engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

CMC-Computers Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Svazek periodika

83

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

475-496

Kód UT WoS článku

001459657000001

EID výsledku v databázi Scopus

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