However, our experiments showed that MO took longer to find optimal hyperparameters compared to FO, especially in complex models where the hyperparameter space is large and evaluating each configuration requires significant computational resources. Although MO adaptively balances exploration and exploitation, it remains computationally intensive. To mitigate this, constraints or refined search spaces can be introduced to limit the number of evaluations needed. Nevertheless, due to the depth and complexity of hyperparameter tuning, MO generally requires more time than FO to reach an optimal solution.