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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.
El‐kenawy et al. (Mon,) studied this question.