Natural-Inspired Data Clustering: A Hybridization between Ant Clustering and Particle Swarm Optimization

The clustering algorithms have evolved over the last decade. With the continuous success of natural inspired algorithms in solving many engineering problems, it is imperative to scrutinize the success of these methods applied to data clustering. These naturally inspired algorithms are mainly stochastic search and optimization techniques, guided by the principles of collective behavior and self-organization of insect swarms. The parameters setting of the ant colony clustering algorithms determine the behavior of each ant and are critical for fast convergence to near optimal solutions of clustering task. This inspired us to explore techniques for automatically learning the optimal parameters for a given clustering task. We devised and implemented a hybrid Ant-Colony clustering algorithm, which uses particle swarm optimization algorithm in the early stages to ‘breed’ a population of ants possessing near optimal behavioral parameter settings for a given problem. This hybrid algorithm converges rapidly for nearly optimal parameters that maximize the ant-colony clustering behavior.