A New Adaptive Inertia Weight Based Multi-objective Discrete Particle Swarm Optimization Algorithm for Community Detection

Abstract

A multi-objective discrete particle swarm optimization (MODPSO) algorithm is useful in accurately identifying communities in a network by avoiding the pitfalls of modularity optimized discrete PSO algorithms. Inertia weights in a PSO can be used to guide the flight of particles in PSO by modifying step size of the particles. In this paper, we present a new adaptive inertia weight based MODPSO and compare it with other good inertia weight approaches by applying them to three real-world datasets. Our algorithm demonstrates consistently the best results among various inertia weight strategies in three real-world datasets with maximum Q (modularity score) values of 0.457, 0.527728 and 0.60457 for Zachary’s Karate Club, Bottlenose Dolphins and American College Football datasets, respectively. Adaptive inertia weight strategy is able to perform consistently by adaptively determining the step size of the velocity update equation. To the best of our knowledge, this is the first such attempt to explore the adaptive inertia weight technique with MODPSO in the field of community detection in complex networks.