Community detection in complex networks plays an important role in mining and analyzing the structure and function of networks. However, traditional algorithms for community detection-based graph partition and hierarchical clustering usually have to face expensive computational costs or require some specific conditions when dealing with complex networks. Recently, community detection based on intelligent optimization attracts more and more attention because of its good effectiveness. In this paper, a new multi-objective ant colony optimization with decomposition (MACOD) for community detection in complex networks is proposed. Firstly, a new framework of multi-objective ant colony algorithm specialized initially for the complex network clustering is developed, in which two-objective optimization problem can be decomposed into a series of subproblems and each ant is responsible for one single objective subproblem and it targets a particular point in the Pareto front. Secondly, a problem-specific individual encoding strategy based on graph is proposed. Moreover, a new efficient local search mechanism is designed in order to improve the stability of the algorithm. The proposed MACOD has been compared with four other state of the art algorithms on two benchmark networks and seven real-world networks including three large-scale networks. Experimental results show that MACOD performs competitively for the community detection problems.
Ant Colony Optimization Based on Random Walk for Community Detection in Complex Networks