A multi-objective discrete cuckoo search algorithm with local search for community detection in complex networks

Detecting community is a challenging task in analyzing networks. Solving community detection problem by evolutionary algorithm is a heated topic in recent years. In this paper, a multi-objective discrete cuckoo search algorithm with local search (MDCL) for community detection is proposed. To the best of our knowledge, it is first time to apply cuckoo search algorithm for community detection. Two objective functions terraced as negative ratio association and ratio cut are to be minimized. These two functions can break through the modularity limitation. In the proposed algorithm, the nest location updating strategy and abandon operator of cuckoo are redefined in discrete form. A local search strategy and a clone operator are proposed to obtain the optimal initial population. The experimental results on synthetic and real-world networks show that the proposed algorithm has better performance than other algorithms and can discover the higher quality community structure without prior information.