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

Author

Listed:
  • Yasir Ahmad
  • Mohib Ullah
  • Rafiullah Khan
  • Bushra Shafi
  • Atif Khan
  • Mahdi Zareei
  • Abdallah Aldosary
  • Ehab Mahmoud Mohamed

Abstract

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.

Suggested Citation

  • Yasir Ahmad & Mohib Ullah & Rafiullah Khan & Bushra Shafi & Atif Khan & Mahdi Zareei & Abdallah Aldosary & Ehab Mahmoud Mohamed, 2020. "SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks," Complexity, Hindawi, vol. 2020, pages 1-9, December.
  • Handle: RePEc:hin:complx:6695032
    DOI: 10.1155/2020/6695032
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    Cited by:

    1. Scianna, Marco, 2024. "The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 357-382.
    2. Abu Saleh Bin Shahadat & M. A. H. Akhand & Md Abdus Samad Kamal, 2022. "Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem," Mathematics, MDPI, vol. 10(14), pages 1-24, July.

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