Exploring Swarm Intelligence for Enhanced Clustering in Artificial Intelligence: A PSO-Kmeans Hybrid Approach - Adbi, Said; Mouncif, Hicham (Revue d'Intelligence Artificielle, 2025)
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.