Discrete Improved Grey Wolf Optimizer for Community Detection

Mohammad H. Nadimi-Shahraki, Ebrahim Moeini, Shokooh Taghian, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
JournalJournal of Bionic Engineering
Publication statusPublished - 2023


  • Community detection
  • Complex network
  • Grey wolf optimizer algorithm
  • Metaheuristic algorithms
  • Optimization
  • Swarm intelligence algorithms


Dive into the research topics of 'Discrete Improved Grey Wolf Optimizer for Community Detection'. Together they form a unique fingerprint.

Cite this