An Enhanced Multi-Objective Evolutionary Algorithm with Decomposition for Signed Community Detection Problem

Abstract

Community detection is useful for better understanding of the structure of complex networks, and aids in the extraction of required information from such networks. Community detection problem can be modelled as an NP-hard combinatorial optimization problem. Many optimization algorithms (both single -objective and multi-objectives) have been implemented to address community detection problem, where the first objective is usually representing the maximization of the internal connections, while the second objective represents the minimization of the external connections between the communities or clusters. In this research, an enhanced mutation operator is proposed for improving the search performance of multi-objective evolutionary algorithm with decomposition (MOEA/D), based on the types of the connections between the nodes. The proposed algorithm was evaluated based on a set of five benchmark datasets, in terms of Normalized Mutual Information (NMI), Weighted NMI (WNMI), Signed Modularity (Qs), and Error rate (Error). The results showed that our proposed enhanced algorithm has attained the best position as compared to the standard version of MOEA/D, and other state of art research papers.