An intelligent ant colony optimization for community detection in complex networks

Many systems in social world can be represented by complex networks. It is of great significance to detect the community structure and analyze the functions for networks. In recent years, plenty of research and works have been focused on this problem. In this paper, we propose an enhanced algorithm based on ant colony optimization (ACO) for the community detection problems. In order to avoid redundant computing in ACO, we divide the ant colony into two groups, original group and intelligent group, which search the solution space simultaneously. In the intelligent group, due to the locus-based adjacency representation of the solution, we let some of them have an ability of self-learning and others can learn from the optimal solutions proactively. Experiments on synthetic and real-life networks show the proposed algorithm can explore in an efficient and stable way.