A Grey Wolf Optimization Algorithm with Triangular Community and Crossover Operator for Community Discovery

Abstract

Community discovery under complex networks is a hot discussion issue in network science research. It is very necessary to find a good community structure to study complex networks. At present, many evolutionary algorithms are used for community discovery. However, prior knowledge is not considered for community detection, and full consideration of the network topology can further improve community discovery performance. Therefore, we propose a new algorithm TM-GWO to optimize community discovery. TM-GWO is based on the gray wolf optimization algorithm. It designs a new initialization method and migration-based crossover operator to realize community discovery. The experimental results show that TM-GWO is better than the current Multi-objective evolutionary algorithm.