aspg-3-3669-1791419430 20261008003030 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 2 El-Sayed M. El-kenawy School of ICT, Faculty of Engineering, Design and Information, Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Applied Science Research Center. Applie Amel Ali Alhussan Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Doaa Sami Khafaga Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Amal H. Alharbi Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Sarah A. Alzakari Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Abdelaziz A. Abdelhamid 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 Te Abdelhameed Ibrahim School of ICT, Faculty of Engineering, Design and Information, Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt 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. 2025 170 186 3669 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190213 https://www.americaspg.com/journal/3/article/3669 https://www.americaspg.com/storage/81741385890.pdf