Improved Ant Colony Optimization for Clustering

This paper presents an improved ant colony optimization (ACO) clustering program that achieves better performance than basic ant colony clustering algorithms. The enhanced clustering program incorporates several innovative strategies and optimization techniques, enabling more efficient and accurate processing of large-scale datasets. Key implementation features include dynamic pheromone update rules, adaptive parameter tuning mechanisms, and optimized path selection probabilities that significantly improve convergence speed. The program demonstrates enhanced convergence properties and stability, allowing it to handle more complex and diverse data patterns effectively. Through comprehensive experimental comparisons with traditional ant colony clustering algorithms, results indicate substantial improvements in clustering performance metrics including cluster purity, silhouette coefficients, and computational efficiency. The algorithm's core functions involve intelligent ant movement simulations with memory-based decision making and local search optimization techniques. This improved ant colony clustering program shows significant potential and practical application value for solving real-world data clustering challenges, particularly in domains requiring robust pattern recognition and data segmentation capabilities. The implementation includes modular components for data preprocessing, cluster center initialization, and iterative optimization cycles with convergence checks.