## 4 Results
Even after 300 epochs, their RMSE values remain significantly higher than other approaches, confirming that neither CNN nor LSTM alone is sufficient for handling the complexities of SEE optimization in CF-mMIMO-based IoT networks. These results reinforce that the MOIBBO-CNN-LSTM model achieves superior learning efficiency and accuracy through its integrated optimization approach, ensuring rapid convergence and minimal prediction error compared to alternative state-of-the-art methods. Figure 7 illustrates the evolution of SEE over training epochs for different algorithms, showcasing their optimization efficiency. Performance trends provide insights into the trade-off between convergence speed and final SEE. MOIBBO-CNN-LSTM achieves the highest SEE, starting at 5.5 Mb/J in the initial epoch and reaching 12.5 Mb/J at epoch 200. In particular, it converges quickly, reaching near-optimal SEE (12 Mb/J) by epoch 80. This rapid improvement highlights its efficient learning capacity and its superior optimization strategy. NSGA-II-CNN-LSTM exhibits a similar trend, although slightly lower in performance, stabilizing around 12 Mb/J at epoch 90. This suggests that NSGA-II-CNN-LSTM provides competitive energy efficiency, making it a viable alternative when considering trade-offs between accuracy and computational complexity. ViT and DRL demonstrate moderate SEE improvements, stabilizing at approximately 10 Mb/J and 9 Mb/J, respectively. These methods exhibit a more gradual learning curve that requires more training epochs to achieve satisfactory performance. CNN and LSTM, on the other hand, show the slowest progression. LSTM starts with the lowest SEE (2.5 Mb/J) and reaches 7.5 Mb/J at epoch 200, stabilizing later than all other models at around epoch 140. CNN follows a similar trajectory but with slightly better SEE convergence. The delayed improvement in these models highlights their limited optimization efficiency in energy management, which is sensitive to security. Figure 8 illustrates the impact of the maximum transmit power of APs, \(P_{AP}^{\max}\), on the SEE performance across different algorithms. The results demonstrate a general increasing trend in SEE as \(P_{AP}^{\max}\) rises, indicating that higher transmission power enhances overall energy efficiency. However, the rate of improvement varies across the algorithms. For lower values of \(P_{AP}^{\max}\) (i.e., 50-200 mW), the SEE of all models exhibits a logarithmic-like growth, showing a rapid increase initially before stabilizing. In this range, the proposed MOIBBO-CNN-LSTM and NSGA-II-CNN-LSTM achieve the highest SEE, reaching ap  
Figure 6: Convergence trends of the proposed methods in terms of RMSE over 300 training epochs. proximately 8.0 Mb/J and 7.5 Mb/J at 200 mW, respectively. The ViT and DRL models also show improvements, though with relatively lower SEE values. CNN and LSTM demonstrate the worst performance, with LSTM only reaching 5.5 Mb/J at 200 mW. As \(P_{AP}^{\max}\) increases beyond 200 mW (up to 500 mW), the growth rate of SEE slows down across all models, following a diminishing return effect. This indicates that after a certain power threshold, increasing \(P_{AP}^{\max}\) does not significantly improve energy efficiency. The proposed MOIBBO-CNN-LSTM consistently maintains the highest SEE, reaching 9.5 Mb/J at \(P_{AP}^{\max}=500\) mW, demonstrating its superior energy efficiency. The NSGA-II-CNN-LSTM model remains competitive with an SEE of approximately 9.0 Mb/J, while ViT and DRL follow closely behind. The LSTM model, however, struggles to exceed 7.0 Mb/J even at higher \(P_{AP}^{\max}\) values. The results depicted in Figure 9 illustrate a downward trend in SEE across all evaluated methods as the number of APs increases. This behavior suggests that while adding more APs enhances SR, the associated power consumption grows at a relatively higher rate, ultimately leading to a net reduction in SEE.