Exploring Swarm Intelligence for Enhanced Clustering in Artificial Intelligence: A PSO-Kmeans Hybrid Approach

In recent years, the integration of swarm intelligence-based metaheuristic optimization techniques into Artificial Intelligence (AI) has garnered significant attention. This project aims to investigate the potential applications of swarm intelligence techniques within the domain of AI. By leveraging...

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Bibliographic Details
Published in:Revue d'Intelligence Artificielle Vol. 39; no. 2; p. 11
Main Authors: Adbi, Said, Mouncif, Hicham
Format: Journal Article
Language:English
French
Published: Edmonton International Information and Engineering Technology Association (IIETA) 30.04.2025
Subjects:
Algorithms
Artificial intelligence
Behavior
Birds
Clustering
Collaboration
Datasets
Evolution & development
Heuristic methods
Methods
Optimization
Particle swarm optimization
Swarm intelligence
Velocity
ISSN:0992-499X, 1958-5748
Online Access:Get full text
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Summary:In recent years, the integration of swarm intelligence-based metaheuristic optimization techniques into Artificial Intelligence (AI) has garnered significant attention. This project aims to investigate the potential applications of swarm intelligence techniques within the domain of AI. By leveraging the collective behavior and adaptive nature of swarm intelligence, these metaheuristic optimization methods offer unique opportunities for solving complex problems in AI. Numerous optimization methods have been proposed in academic research to address clustering-related challenges, but swarm intelligence has established a prominent position in the field. Particle swarm optimization (PSO) is the most popular swarm intelligence technique and one of the researchers' favorite areas. In this study, we introduce a novel clustering approach that integrates PSO with the K-means algorithm, aimed at enhancing clustering outcomes by effectively addressing common clustering challenges. The PSO algorithm has been shown to converge successfully during the initial stages of a global search, but around the global optimum. The proposed algorithm is designed to organize a given dataset into multiple clusters. To assess its effectiveness, we tested the algorithm on five different datasets. We then compared its clustering performance with that of the K-means and PSO algorithms, evaluating it based on metrics such as execution time, accuracy, quantization error, and both intra-cluster and inter-cluster distances.
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ISSN:0992-499X
1958-5748
DOI:10.18280/ria.390201