A multi-objective ant colony optimization algorithm for community detection in complex networks

Studying the structure of the evolutionary communities in complex networks is essential for detecting the relationships between their structures and functions. Recent community detection algorithms often use the single-objective optimization criterion. One such criterion is modularity which has fundamental problems and disadvantages and does not illustrate complex networks’ structures. In this study, a novel multi-objective optimization algorithm based on ant colony algorithm (ACO) is recommended to solve the community detection problem in complex networks. In the proposed method, a Pareto archive is considered to store non-dominated solutions found during the algorithm’s process. The proposed method maximizes both goals of community fitness and community score in a trade-off manner to solve community detection problem. In the proposed approach, updating the pheromone in ACO has been changed through Pareto concept and Pareto Archive. So, only non-dominated solutions that have entered the Pareto archive after each iteration are updated and strengthened through global updating. In contrast, the dominated solutions are weakened and forgotten through local updating. This method of updating the Pheromone will improve algorithm exploration space, and therefore, the algorithm will search and find new solutions in the optimal space. In comparison to other algorithms, the results of the experiments show that this algorithm successfully detects network structures and is competitive with the popular state-of-the-art approaches.