SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks

Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study propose...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-9
Main Authors: Mohamed, Ehab Mahmoud, Zareei, Mahdi, Khan, Atif, Shafi, Bushra, Khan, Rafiullah, Ullah, Mohib, Ahmad, Yasir, Aldosary, Abdallah
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Biology
Cluster analysis
Computer science
Function words
Genetic algorithms
Linguistics
Methods
Modularity
Motion perception
Optimization algorithms
Optimization techniques
Particle swarm optimization
Social networks
Social sciences
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Summary:Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm’s performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/6695032