An Enhanced Bat Algorithm Using Clustering Coefficient for Community Detection in Complex Networks

The discrete bat algorithm has gained significant interest in solving a variety of combinatorial optimization problems. Discovering community structures in complex networks is particularly challenging, as it is an NP-hard problem. To address this, various adaptations of the discrete bat algorithm have been developed. This work proposes CC-DBAT, an improved discrete bat algorithm utilizing the clustering coefficient to identify community structures in complex networks. The proposed algorithm generates an initial population of solutions using the clustering coefficient metric, followed by a fast local move technique to enhance the intensification process. Additionally, a random neighbor communities technique is employed to generate a random solution that guides the search process toward the global optimum. Experiments conducted on synthetic and real-world networks demonstrate that the proposed approach outperforms existing bat algorithms and exhibits improved convergence rates.