## 5 Discussion  
Table 3 presents a comparative analysis of different models in optimizing SEE in CF-mMIMO-based IoT networks. The table evaluates each algorithm based on RMSE, R\({}^{2}\), MAPE, and the average execution time (in seconds). The results indicate that the proposed MOIBBO-CNN-LSTM model consistently outperforms the baseline approaches, achieving the lowest RMSE (0.08), the highest R\({}^{2}\) score (0.97), and the lowest MAPE (1.03%), demonstrating its superior predictive performance in accurately modeling SEE trade-offs in CF-mMIMO networks. The comparative analysis highlights the limitations of standalone DL models such as CNN and LSTM, which exhibit significantly higher RMSE values (11.27 and 13.91, respectively) and lower R\({}^{2}\) scores (0.82 and 0.80), confirming their inability to fully capture the complex relationships between energy efficiency and security constraints. The NSGA-II-CNN-LSTM model, while performing better than conventional DL models, still struggles with an RMSE of 3.27 and a MAPE of 6.96%, indicating that the multi-objective optimization strategy of MOIBBO plays a critical role in improving the accuracy of the model. The ViT and DRL models, which use transformer-based and reinforcement learning techniques, also fail to achieve the same level of accuracy, with higher error rates and suboptimal SEE predictions compared to the proposed model. From a computational efficiency perspective, the results demonstrate that MOIBBO-CNN-LSTM not only achieves superior accuracy but also maintains a reasonable execution time of 962 s, outperforming NSGA-II-CNN-LSTM (1241 s) and DRL (1317 s) in terms of computational cost. While CNN and LSTM models execute faster (652 s and 743 s, respectively), their inferior accuracy makes them impractical for real-world deployment. The ViT model, despite maintaining balanced performance, still suffers from higher computational demands (1012 s) while underperforming in accuracy (RMSE = 6.12, MAPE = 8.32%). The superior performance of MOIBBO-CNN-LSTM can be attributed to its integration of  
Figure 9: SEE performance as a function of the number of APs for different algorithms. CNN for spatial feature extraction, LSTM for sequential modeling, and MOIBBO for multi-objective optimization, which collectively ensures optimal trade-offs between accuracy, efficiency, and model complexity. Unlike traditional optimization techniques, MOIBBO dynamically adjusts the network's architecture, hyperparameters, and feature selection, leading to faster convergence, improved generalization, and lower prediction errors. These results confirm that MOIBBO-CNN-LSTM is the most effective framework for optimizing SEE in CF-mMIMO-based IoT networks, providing both high predictive accuracy and efficient computational performance, making it well-suited for real-time IoT network optimization and secure energy management applications. Table 4 presents a comparative analysis of algorithm execution times based on RMSE termination conditions, demonstrating how efficiently each model converges to different levels of prediction accuracy. The table records the run time (in seconds) required for each model to reach the predefined RMSE thresholds, namely RMSE \(<\) 20, RMSE \(<\) 15, RMSE \(<\) 10, and RMSE \(<\) 5. These values indicate the rate of convergence for each approach, with lower execution times reflecting higher efficiency in achieving a given accuracy level. In particular, MOIBBO-CNN-LSTM is the only method that successfully reaches RMSE \(<\) 5, requiring just 386 s, while all other models fail to converge to this level within a reasonable time frame. The results indicate that MOIBBO-CNN-LSTM consistently outperforms all baseline models in both convergence speed and final accuracy. For an RMSE threshold of less than 20, the proposed model achieves convergence in just 75 s, significantly faster than NSGA-II-CNN-LSTM (201 s), ViT (312 s), and DRL (365 s).