Penguins Search Optimization Algorithm for Community Detection in Complex Networks

Mohamed Guendouz, Abdelmalek Amine and Reda Mohamed Hamou
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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

International Journal of Applied Metaheuristic Computing (IJAMC), 2018, vol. 9, issue 1, 1-14

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.

Date: 2018
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