Community detection is one of the major topics in the study of complex networks, which aims to uncover their structural properties. Recently, many evolutionary methods have been successfully employed to identify communities of complex networks. Community detection has been treated so far as a single or multi-objective problem in evolutionary-based approaches. Since each objective covers a specific aspect of the network's properties, it could result in identification of better community structures to investigate the problem with more than two objectives. In this paper, we proposed a method referred to as MaOCD that formulates community detection as a many objective task. MaOCD uses an ideal-point based strategy to guide the population towards an optimal community structure. The main purpose is to take advantage of optimizing several objectives simultaneously and using a representation that reduces the search space. This enhances the convergence of the method, and automatically determines the number of modules. We introduced a novel metric called IGDC that gives multi/many-objective community detection methods the capability of being comparable regarding multiple objectives. Several experiments were carried out on synthetic and real-world datasets to show the performance of our method. The results demonstrated that MaOCD successfully detected the communities in the network structure compared to some state-of-the-art single and multi-objective methods.
