Particle Swarm Optimization (PSO) Algorithm for Enhanced K-means Clustering

This documentation presents a MATLAB implementation that utilizes Particle Swarm Optimization (PSO) to enhance K-means clustering performance. The code has been thoroughly tested and validated on MATLAB versions 6.5/7.1, though compatibility with other versions remains unverified but is expected to function properly. The implementation features a complete PSO algorithm integration that optimizes K-means clustering parameters and centroid initialization. Key algorithmic components include: - Particle position encoding for cluster centroids - Velocity update mechanisms with inertia weights - Fitness evaluation using within-cluster sum of squares (WCSS) - Global and personal best position tracking Through PSO optimization, the algorithm effectively explores the solution space to identify optimal cluster configurations, significantly improving clustering accuracy and convergence properties compared to standard K-means. The code structure includes modular functions for particle initialization, fitness calculation, and swarm updating, allowing for straightforward customization of parameters such as swarm size, iteration count, and convergence criteria. Additional technical specifications and implementation details are available upon request to facilitate better understanding and utilization of this optimization approach for clustering applications.