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Fusion: Practice and Applications

Volume 19 , Issue 2 , PP: 170-186, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection

El-Sayed M. El-kenawy 1 * , Amel Ali Alhussan 2 , Doaa Sami Khafaga 3 , Amal H. Alharbi 4 , Sarah A. Alzakari 5 , Abdelaziz A. Abdelhamid 6 , Abdelhameed Ibrahim 7 , Marwa M. Eid 8

  • 1 School of ICT, Faculty of Engineering, Design and Information, Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, Jordan - (sayed.elkenawy@polytechnic.bh)
  • 2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (aaalhussan@pnu.edu.sa)
  • 3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (dskhafga@pnu.edu.sa)
  • 4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (ahalharbi@pnu.edu.sa)
  • 5 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (saalzakari@pnu.edu.sa)
  • 6 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt; Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Shaqra, Saudi Arabia - (abdelaziz@cis.asu.edu.eg)
  • 7 School of ICT, Faculty of Engineering, Design and Information, Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain - (abdelhameed.fawzy@polytechnic.bh)
  • 8 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt - (mmm@ieee.org)

    • Doi: https://doi.org/10.54216/FPA.190213

    Received: December 17, 2024 Revised: February 09, 2025 Accepted: March 03, 2025
    Abstract

    As optimization tasks become increasingly complex, particularly in feature selection, there is a growing need for algorithms capable of robustly balancing exploration and exploitation. In this work, we propose the Binary Swordfish Movement Optimization Algorithm (BSMOA), inspired by the synchronized and agile movements of swordfish. BSMOA employs adaptive parameters to navigate high-dimensional search spaces through dynamic exploration, exploitation, and elimination stages. Extensive experiments on benchmark datasets demonstrate that BSMOA outperforms state-of-the-art algorithms, including bHHO, bGWO, and bPSO, regarding average error, feature reduction, and computational efficiency. Key contributions of BSMOA include its improved balance between global and local search and its ability to achieve stable and accurate feature selection. This work has broad implications for applications in machine learning, engineering design, and other optimization domains, providing a reliable tool for tackling challenging binary optimization problems.

    Keywords :

    Binary optimization , Feature selection , Novel metaheuristic algorithm , Swordfish Movement Optimization , Exploration-exploitation balance

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    Cite This Article As :
    M., El-Sayed. , Ali, Amel. , Sami, Doaa. , H., Amal. , A., Sarah. , A., Abdelaziz. , Ibrahim, Abdelhameed. , M., Marwa. A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection. Fusion: Practice and Applications, vol. , no. , 2025, pp. 170-186. DOI: https://doi.org/10.54216/FPA.190213
    M., E. Ali, A. Sami, D. H., A. A., S. A., A. Ibrahim, A. M., M. (2025). A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection. Fusion: Practice and Applications, (), 170-186. DOI: https://doi.org/10.54216/FPA.190213
    M., El-Sayed. Ali, Amel. Sami, Doaa. H., Amal. A., Sarah. A., Abdelaziz. Ibrahim, Abdelhameed. M., Marwa. A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection. Fusion: Practice and Applications , no. (2025): 170-186. DOI: https://doi.org/10.54216/FPA.190213
    M., E. , Ali, A. , Sami, D. , H., A. , A., S. , A., A. , Ibrahim, A. , M., M. (2025) . A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection. Fusion: Practice and Applications , () , 170-186 . DOI: https://doi.org/10.54216/FPA.190213
    M. E. , Ali A. , Sami D. , H. A. , A. S. , A. A. , Ibrahim A. , M. M. [2025]. A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection. Fusion: Practice and Applications. (): 170-186. DOI: https://doi.org/10.54216/FPA.190213
    M., E. Ali, A. Sami, D. H., A. A., S. A., A. Ibrahim, A. M., M. "A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection," Fusion: Practice and Applications, vol. , no. , pp. 170-186, 2025. DOI: https://doi.org/10.54216/FPA.190213

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