Hybrid Algorithm Combining Particle Swarm Optimization and K-means Clustering

In the field of computer science, both Particle Swarm Optimization (PSO) and K-means clustering algorithms are highly popular optimization and clustering techniques. These algorithms have been extensively applied across various domains such as artificial intelligence, machine learning, and data mining. Due to their distinct characteristics and limitations, combining them can yield superior results. The hybrid algorithm merges PSO's global optimization capabilities with K-means' efficient clustering mechanism. Implementation typically involves using PSO to optimize initial cluster centroids before applying K-means refinement, or employing PSO to enhance K-means' convergence properties. This hybrid approach demonstrates exceptional performance in optimization and clustering tasks, particularly when handling large-scale and high-dimensional datasets. Growing numbers of researchers and engineers are now implementing this hybrid algorithm with Python/Matlab libraries like scikit-learn to solve diverse complex problems, incorporating fitness functions that evaluate cluster quality metrics such as silhouette scores or within-cluster sum of squares.