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Penguins Search Optimization Algorithm for Community Detection in Complex Networks

Penguins Search Optimization Algorithm for Community Detection in Complex Networks

Mohamed Guendouz, Abdelmalek Amine, Reda Mohamed Hamou
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 14
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522544524|DOI: 10.4018/IJAMC.2018010101
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MLA

Guendouz, Mohamed, et al. "Penguins Search Optimization Algorithm for Community Detection in Complex Networks." IJAMC vol.9, no.1 2018: pp.1-14. http://doi.org/10.4018/IJAMC.2018010101

APA

Guendouz, M., Amine, A., & Hamou, R. M. (2018). Penguins Search Optimization Algorithm for Community Detection in Complex Networks. International Journal of Applied Metaheuristic Computing (IJAMC), 9(1), 1-14. http://doi.org/10.4018/IJAMC.2018010101

Chicago

Guendouz, Mohamed, Abdelmalek Amine, and Reda Mohamed Hamou. "Penguins Search Optimization Algorithm for Community Detection in Complex Networks," International Journal of Applied Metaheuristic Computing (IJAMC) 9, no.1: 1-14. http://doi.org/10.4018/IJAMC.2018010101

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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.

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