Journal of Bionic Engineering (2023) 20:2331–2358 https://doi.org/10.1007/s42235-023-00387-1

Discrete Improved Grey Wolf Optimizer for Community Detection 

Mohammad H. Nadimi?Shahraki1,2 · Ebrahim Moeini1,2 · Shokooh Taghian1,2 · Seyedali Mirjalili3,4

Mohammad H. Nadimi?Shahraki nadimi@iaun.ac.ir; nadimi@ieee.org

1 Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran

2 Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran

3 Centre for Artificial Intelligence Research and Optimisation, Torrens University, Brisbane 4006, Australia

4 Yonsei Frontier Lab, Yonsei University, Seoul, South Korea

Abstract：Detecting communities in real and complex networks is a highly contested topic in network analysis. Although many metaheuristic-based algorithms for community detection have been proposed, they still cannot effectively fulfill large-scale and real-world networks. Thus, this paper presents a new discrete version of the Improved Grey Wolf Optimizer (I-GWO) algorithm named DI-GWOCD for effectively detecting communities of different networks. In the proposed DI-GWOCD algorithm, I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution. Then a novel Binary Distance Vector (BDV) is introduced to calculate the wolves’ distances and adapt I-GWO for solving the discrete community detection problem. The performance of the proposed DI-GWOCD was evaluated in terms of modularity, NMI, and the number of detected communities conducted by some well-known real-world network datasets. The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests. The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.

Keywords ：Community detection · Complex network · Optimization · Metaheuristic algorithms · Swarm intelligence algorithms · Grey wolf optimizer algorithm