Complex Network Community Detection by Improved Nondominated Sorting Genetic Algorithm

Abstract

Aimed at the problems of low solution precision and easy to be trapped into local optima by single objective evolutionary algorithm, a self-adaptive multi-objective optimization algorithm based on nondominated sorting genetic algorithm II (NSGA2) and Label Propagation Algorithm (LPA) is proposed. The algorithm takes Kernel K-means (KKM) and Ratio Cut (RC) as the objective functions. Two new crossover operator and the improved mutation operator is used to achieve the evolution of the population. We conducted simulation experiments in the computer-generated networks and the real-world networks environment. The results show that compared with other community detection algorithms, our algorithm has the advantages of high resolution and strong search ability, and it can effectively identify the community structure in complex networks.