وحید برادران - Assistant Prof. of Industrial Engineering, Islamic Azad University, North Tehran Branch, Iran
امیرحسین حسینیان - Ph.D. Candidate of Industrial Engineering, Islamic Azad University, North Tehran Branch, Iran
رضا درخشانی - Ph.D. Candidate of Industrial Engineering, Islamic Azad University North Tehran Branch, Iran

Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies.

Community Detection, Genetic Algorithm, Optimization, social networks, TOPSIS