Community Detection in Complex Network Using Improved Hybrid Harris Hawk and Coot Bird Optimization Algorithm
International journal of intelligent engineering and systems · 2024
Community detection in complex networks plays a pivotal role in identifying the structural composition of nodes, enabling us to pinpoint key topological features critical for a multitude of applications.Usually, the complex networks are in graphical format in which graph nodes denote objects and edges denote a connection between two things.The existing techniques have limitations such as early convergence and local optima issues.To overcome this issue, this manuscript proposed an Improved Hybrid Harris Hawk and Coot Bird Optimization (IHHHCBO) algorithm in community detection.The optimum separate movement of CBO is incorporated in HHO population initialization to strengthen both optimizations.It is employed to initialize the population for enhancing the position of diversity and change with other individuals.The Karate, Dolphin, Football and Political Books datasets are considered for assessing the IHHHCBO performance.The Ensemble Mutation Strategy (EMS) is developed to produce mutant candidate locations which enhances the exploration and population diversity of optimization.The Normalized Mutual Information (NMI), and Modularity (Q) are considered fitness functions in this research.The IHHHCBO performance is estimated through metrics like NMI, Q, f1-score and accuracy.The IHHHCBO reaches better accuracy of 1, 1, 0.998 and 0.883 for Karate, Dolphin, Football and Political Books respectively which is better when compared to existing algorithms like Core Structure Extraction Algorithm (CSEA), and Modified Crossover Opposition-based Genetic Algorithm (MCOBGA).