DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy the continuous are purposely adapted in DMFO-CD such that it can solve detection. adaptation, locus-based adjacency used to represent position moths flames, initialization process performed by considering structure relation between nodes without need any knowledge about number communities. Solution vectors updated using single-point crossover distance imitating, two-point calculate movement, neighbor-based mutation enhance exploration balance exploitation. fitness function also defined based on modularity. performance was evaluated eleven real-world networks, obtained results were compared with five well-known algorithms detection, including GA-Net, DPSO-PDM, GACD, EGACD, DECS terms modularity, NMI, detected Additionally, statistically analyzed Wilcoxon signed-rank Friedman tests. comparison other comparative algorithms, show proposed competitive detect correct communities high