Community detection is one of the main challenges in social network analysis. Since the issue of community detection is considered as a NP-hard problem, Evolutionary algorithms have been used as one of the most effective approaches. In this paper, a multi-objective particle swarm optimization algorithm and its extended versions are proposed. The aforementioned algorithm uses an opposition-based method for producing an initial swarm. It optimizes two objective functions at the same time which represents a partition of the network as well as using a mutation operator for handling the problem in high dimensions. The performances of the proposed algorithm and its extended versions have been evaluated on real networks. The result represented the efficiency of proposed methods. Also an optimum value is suggested for aforementioned algorithm that can be said with less complexity and calculations, proposed algorithm achieved the acceptable amount of accuracy. The remarkable thing is the better performance of algorithm as the size of social network grows.
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