aspg-28-3236-1792155102 20261016125142 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2024 8 1 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; Jadara University Research Center, Jad Faris H. Rizk Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Ahmed Mohamed Zaki Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Mahmoud Elshabrawy Mohamed Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Abdelhameed Ibrahim School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain 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 Nima Khodadadi Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA Ehab M. Almetwally Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt The Football Optimization Algorithm (FbOA) is introduced as a novel population-based metaheuristic optimization technique inspired by the dynamic strategies of a football team. Designed to address complex optimization problems characterized by high dimensionality, nonlinearity, and multiple local optima, FbOA draws on the strategic balance between exploration and exploitation observed in football gameplay. The algorithm mimics players’ tactical positioning and movement, incorporating short passes, long passes, and positional adjustments to explore and exploit the solution space effectively. This study comprehensively evaluates the performance of FbOA using benchmark functions from the CEC 2005 test suite with 30-dimensional and 100- dimensional optimization problems. The results demonstrate that FbOA outperforms several state-of-the-art metaheuristic algorithms regarding convergence speed, accuracy, and robustness. The findings suggest that FbOA offers a promising alternative for solving various optimization challenges across multiple fields. 2024 21 38 3236 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.080103 https://www.americaspg.com/journal/28/article/3236 https://www.americaspg.com/storage/11729543348.pdf