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Penguins Search Optimization Algorithm for Community Detection in Complex Networks

Author

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  • Mohamed Guendouz

    (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saïda, Saïda, Algeria)

  • Abdelmalek Amine

    (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saïda, Saïda, Algeria)

  • Reda Mohamed Hamou

    (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saïda, Saïda, Algeria)

Abstract

In the last decade, the problem of community detection in complex networks has attracted the attention of many researchers in many domains, several methods and algorithms have been proposed to deal with this problem, many of them consider it as an optimization problem and various bio-inspired algorithms have been applied to solve it. In this work, the authors propose a new method for community detection in complex networks using the Penguins Search Optimization Algorithm (PeSOA), the authors use the modularity density evaluation measure as a function to maximize and they propose also to enhance the algorithm by using a new initialization strategy. The proposed algorithm has been tested on four popular real-world networks; experimental results compared with other known algorithms show the effectiveness of using this method for community detection in social networks.

Suggested Citation

  • Mohamed Guendouz & Abdelmalek Amine & Reda Mohamed Hamou, 2018. "Penguins Search Optimization Algorithm for Community Detection in Complex Networks," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 9(1), pages 1-14, January.
    • Handle: RePEc:igg:jamc00:v:9:y:2018:i:1:p:1-14
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    Cited by:

    1. Marwa Koubaa & Mohamed Haykal Ammar & Noura Beji, 2022. "Solving a Real Case of Seafaring Staff Scheduling Problem Using Cuckoo Optimization Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-19, January.

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