aspg-28-3236-1793997278 20261106203438 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 Faris H. Rizk Ahmed Mohamed Zaki Mahmoud Elshabrawy Mohamed 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 Nima Khodadadi Ehab M. Almetwally Marwa M. Eid 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 15 28 3236 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.080103 https://americaspg.com/journal/28/article/3236 https://americaspg.com/storage/articles/manuscripts/01M5B2B4H3HAPTAV856J966ABJ.pdf