## 4 Results
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{P_{i}-O_{i}}{O_{i}}\right|\times 100 \tag{44}\]\[R^{2}=\frac{\left[\frac{1}{N}\sum_{i=1}^{N}(P_{i}-\bar{P})(O_{i}-\bar{O})\right]^{ 2}}{\sigma_{P}\sigma_{O}} \tag{45}\]  
where \(N\) is the number of observations; \(P_{i}\) is the calculated parameter; \(O_{i}\) is the observed parameter; \(\bar{P}\) is the average calculated parameter; \(\bar{O}\) is the average observed parameter; \(\sigma_{P}\) is the standard deviation of predictions; and \(\sigma_{O}\) is the standard deviation of observations. Beyond accuracy-based metrics, the evaluation also considers the convergence trend of the models. The rate at which the model reaches its optimal performance is a crucial factor in determining its efficiency. A model that converges quickly requires fewer computational resources and can adapt to dynamic environments in real time, which is essential for IoT applications where rapid decision-making is required. The convergence behavior of each model is analyzed across multiple generations of optimization, ensuring that the proposed method is not only accurate but also computationally feasible for real-world deployment. Another critical factor in the evaluation process is execution time, which reflects the total computational cost of training and inference. Since IoT networks operate under strict latency and power constraints, it is necessary to ensure that the proposed model remains computationally efficient while delivering high performance. A model that requires excessive training time may not be practical for real-time network management, particularly in large-scale CF-mMIMO systems where quick adaptation is essential. Therefore, execution time is carefully monitored to assess the trade-off between performance and computational feasibility. These evaluation metrics were carefully selected to align with the core objective of the study, which is to enhance SEE in CF-mMIMO-based IoT networks. Hyperparameter calibration is a critical aspect of DL and optimization-based models, as it directly influences the performance, convergence speed, and generalization ability of an algorithm. In complex architectures such as CNN-LSTM and transformer-based models, selecting the appropriate hyperparameters ensures that the model can efficiently extract spatial and temporal patterns while maintaining computational efficiency. Poorly tuned hyperparameters may lead to underfitting or overfitting, where the model either fails to learn meaningful representations or memorizes training data without generalizing well to new inputs. In optimization-driven frameworks such as MOIBBO and NSGA-II, hyperparameter calibration affects the balance between exploration and exploitation, determining how efficiently the search space is navigated for optimal solutions. Several approaches exist for hyperparameter tuning, with Grid Search, Random Search, and Bayesian optimization being the most commonly used techniques. Grid Search is a systematic method that exhaustively tests all possible combinations of hyperparameters within a predefined range, ensuring that the optimal set of parameters is identified. This method is highly effective for structured search spaces but becomes computationally expensive as the number of hyperparameters increases. Table 2 provides the optimized hyperparameter configurations for the proposed MOIBBO-CNN-LSTM model and the baseline comparison methods, including ViT, DRL, CNN, LSTM, and NSGA-II, fine-tuned using the grid search approach to ensure optimal performance. For MOIBBO-CNN-LSTM, key hyperparameters such as learning rate (0.003), dropout rate (0.2), batch size (64), number of convolutional layers (10), and kernel size (\(5\times 5\)) were fine-tuned to maximize performance. Additionally, the mutation rate (0.06), population size (100), and iteration limit (300) for the MOIBBO optimizer were calibrated to balance search efficiency and convergence speed.