International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET)

37 / 2017-01-21 10:15:31

A Hybrid Biogeography-based Optimization and Differential Evolution Algorithm for Community Detection in Complex Networks

12587

Community detection is a particularly important research subject in complex networks. In this paper, we use the hybrid Biogeography-based Optimization and Differential Evolution (BBO/DE) algorithm for community detection. In the previous work, we have proved that the BBD/DE algorithm has a better convergence ability of optimization. The BBD/DE algorithm does not need to know how many communities the network has. And the modularity function is the only criterion to evaluate community detection. We use the modularity function as the fitness function of the BBD/DE algorithm. As a result, the optimization algorithm can be combined with the community detection of complex network together well. As the performance measure, we choose both real network and computer generated network. Experimental results show that the BBO/DE algorithm can be used for community detection, and can achieve similar results with the traditional algorithms.