An Ant Colony Clustering Algorithm

This paper presents an ant colony clustering algorithm for optimally clustering N objects into K clusters. The algorithm employs the global pheromone updating and the heuristic information to construct clustering solutions and uniform crossover operator to further improve solutions discovered by ants. This algorithm has been implemented and tested on several simulated and real datasets. The performance of this algorithm is compared with other popular heuristic methods. Our computational simulations reveal very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required.