Multi-objective optimization of community detection using discrete teaching-learning-based optimization with decomposition

Community detection has been an active field of study in complex network analysis in recent years. It can be modeled as a seriously nonlinear optimization problem. Many intelligent optimization techniques have shown promising results for this problem. The teaching-learning-based optimization (TLBO) algorithm is a recently proposed swarm intelligent algorithm. In this paper, a discrete variant of TLBO (DTLBO) is proposed to address discrete optimization problems. In the proposed method, the learner representation scheme is redefined, and the updating rules for learners are also redesigned. Moreover, based on the proposed discrete variant DTLBO, a multi-objective discrete method (MODTLBO/D) is proposed to solve community detection problems for complex networks. The multi-objective decomposition mechanism is adopted and neighbor-based mutation is introduced to maintain the diversity of the population and avoid being trapped in the local optima. Finally, to verify the performance of the proposed algorithm, real-world networks are examined. The experimental results indicate that MODTLBO/D is effective compared with other algorithms used for community detection in complex networks.