An Adaptive Ant Colony Clustering Algorithm and Application in the TSP

Abstract

Clustering is the process of partitioning or grouping a given set of patterns into disjoint clusters. Clustering has been a widely studied problem in a variety of application domains including data mining, knowledge discovery, artificial intelligence and etc. As a swarm-intelligent method, the ant colony clustering algorithm (ACCA) is inspired by the behavior of ant colonies. In the ACCA, ants are allowed to move randomly, pick up and drop objects in clusters at certain probability, which depends on similarity with surrounding objects. Ant Colony optimization (ACO) is being used by many applications for past few years. In this paper, an effective clustering algorithm based Ant Colony optimization (ACO), in which adaptive strategy is integrated, is proposed. ACCA is applied to solving TSP. The experiment results indicate that the proposed algorithm is effective.