Basic Ant Colony Clustering Algorithm and Enhanced Versions with MATLAB Source Code

This article examines fundamental ant colony clustering algorithms and their optimized variants. While basic ant colony clustering demonstrates effective problem-solving capabilities for clustering tasks, it occasionally encounters convergence challenges. To resolve this, improved algorithms have been developed - notably a mutation-integrated ant colony algorithm. This enhancement builds upon genetic algorithm frameworks by introducing mutation factors that accelerate convergence through diversified solution exploration. The MATLAB implementation includes key functions for pheromone updating, probability calculations, and mutation operations using rand() functions for stochastic selection. Attached visualizations demonstrate the algorithm's clustering performance across various datasets. Complete MATLAB source code is provided, featuring modular architecture with separate functions for initialization, ant movement simulation, and cluster validation metrics.