An Optimized Ant System For Clustering With Elitist Ant And Local Search

Clustering analysis is an important field in data mining, and also one of the current research hotspots in computer science. This paper focus on some classical data clustering algorithms and swarm intelligence, especially ant colony optimization, trying to combine these two kinds of algorithms and improve the efficiency and accuracy of data clustering. This paper proposes a new ant colony optimization data clustering algorithm, named ant colony clustering algorithm with elitist ant and local search (ACC-EAL). This algorithm adopts a new pheromone incremental calculation method, making the distances among the clusters tend to increase, and the clusters get denser. Meanwhile local search provides the ants more opportunity to find optimal solution and the elite ant strategy makes the ants with optimal solutions contribute more to the pheromone increment.