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Abstract

Nowadays the characteristic of many systems can be captured and investigated as networks of connected communities. Recently, large research interests are devoted towards unraveling natural divisions in such complex networks. Due to problem complexity, the field of multi-objective evolutionary algorithms (MOEAs) reveals outperformed results, however, they lack the introduction of some problem-specific heuristic operators that realize their principles from the natural structure of communities. The main contribution of this paper is to introduce a heuristic perturbation operator that can as a local search operator. Thewell known multi-objective evolutionary algorithm with decompositions (MOEA/D) is adopted with the proposed perturbation operator to identify the overlapped community sets in complex networks. The performance of the proposed MOEA/D is evaluated under a set of experiments on real-world social networks of different complexities. The results prove the positive impact of the proposed heuristic operator to harness the strength of MOO model in both terms of convergence velocity and convergence reliability.