Abstract: As an important property of complex networks, community structure is of great significance for understanding the function and organization of networks.In order to solve the community discovery problem of complex networks, a multi-objective five-elements cycle optimization algorithm (MOFECO) is proposed.Firstly, the task of the community discovery is modelled as a multi-objective optimization problem, and two opposite targets, inverse ratio vssociation (IRA) and ratio cut (RC) are selected as the objective function.Then, based on five-elements cycle model (FECM), individual updating is implemented through local optimal individuals and global optimal individuals, and crossover and mutation operators are introduced to improve the update strategy.Finally, the fast non-dominated sorting method is used to obtain the Pareto optimal community partitioning set, which is helpful to reveal the hierarchical structure of complex networks.Experiments are conducted on LFR benchmark networks and real social networks, compared with single-objective algorithms(GA-Net, Meme-Net) and multi-objective algorithms, such as MOGA-Net, MOCD, MOEA/D-Net, DMOPSO, DIM-MOEA/D and MOCD-ACO, MOFECO overcomes the shortcomings of traditional single-objective optimization of community structure division, and improves the accuracy of community discovery.

Key words: Community discovery, Complex network, Evolutionary algorithm, Five-elements cycle optimization, Multi-objective optimization