Community structure is one of the most important topological characteristics of complex networks. Detecting the community structure is a highly challenging problem in analyzing complex networks and it has high significance for understanding the function and organization of complex networks. A wide range of algorithms for this problem uses the maximization of a quality function called modularity. In this paper, a Chaotic Memetic Algorithm is proposed and used to solve the problem of the community structure detection in complex networks. In the proposed algorithm, the combination of the genetic algorithm (global search) and a dedicated local search is used to search the solution space. In addition, to improve the convergence speed and efficiency, in both global search and local search processes, instead of random numbers, chaotic numbers are used. By using chaotic numbers, the population diversity is preserved and it prevents from falling in the local optimum. The experiments on both real-world and synthetic benchmark networks indicate that the proposed algorithm is effective compared with state-of-the-art algorithms.
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