## 4 Results
The ViT model was configured with six attention heads, ten transformer layers, and a GELU activation function, which are optimal settings for handling complex spatial and temporal dependencies. For DRL, hyperparameters such as \(\varepsilon\)-greedy exploration (ranging from 0.19 to 0.89), discount factor (0.91), and batch size (64) were tuned to ensure an optimal balance between learning and exploration. The NSGA-II-based model was configured with combination probability (0.94)  
and mutation probability (0.08), with a population size of 100 and 300 iterations, ensuring robust performance in multi-objective optimization. The results of this calibration process demonstrate the effectiveness of grid search in identifying optimal configurations, as each model was fine-tuned to achieve maximum predictive accuracy, computational efficiency, and stability. The hyperparameter tuning process significantly improved the convergence behavior and final performance of the models, ensuring that they operate at peak efficiency when applied to real-world IoT network optimization. Figure 6 illustrates the convergence behavior of all evaluated models by plotting the RMSE values against training epochs, providing insight into their learning efficiency and optimization stability. The curves represent how each algorithm improves over time, with a steeper decline indicating faster convergence and better learning performance. The MOIBBO-CNN-LSTM model (blue line) demonstrates the most rapid and stable convergence, reaching near-optimal RMSE values within the first 100 epochs and continuing to refine its accuracy until it achieves the lowest RMSE by epoch 300. In contrast, the other models exhibit significantly slower convergence rates and higher final error values, emphasizing the advantage of the proposed multi-objective optimization strategy. The NSGA-II-CNN-LSTM model (green line) shows a moderate convergence rate, steadily decreasing its RMSE over training iterations but ultimately stabilizing at a higher error level compared to MOIBBO-CNN-LSTM. This suggests that while evolutionary optimization aids the CNN-LSTM structure, it lacks the adaptability and efficiency of MOIBBO's dynamic search mechanisms. The ViT and DRL models demonstrate a slower learning \begin{table}
\begin{tabular}{c l c} \hline \hline
**Proposed Methods** & **Parameters** & **Value** \\ \hline \multirow{8}{*}{MOIBBO-CNN–LSTM} & Learning rate & 0.003 \\  & Recurrent dropout rate & 0.2 \\  & Sequence length & 8 \\  & Batch size & 64 \\  & Number of convolution layers & 10 \\  & Kernel size & \(5\times 5\) \\  & Pooling type & Max pooling (\(3\times 3\)) \\  & Optimizer & MOIBBO \\  & Mutation rate & 0.06 \\  & Population size & 100 \\  & Iteration & 300 \\ \hline \multirow{8}{*}{ViT} & Learning rate & 0.005 \\  & Patch size & 16 \(\times\) 16 \\  & Number of attention heads & 6 \\  & Number of Transformer layers & 10 \\  & Activation function & GELU \\  & Dropout rate & 0.1 \\  & Optimizer & Adam \\ \hline \multirow{8}{*}{DRL} & Learning rate & 0.004 \\  & Discount factor & 0.91 \\  & \(\varepsilon\)-greedy & 0.19–0.89 \\ \cline{1-1}  & Batch size & 64 \\ \cline{1-1}  & Number of hidden layers & 6 \\ \cline{1-1}  & Number of neurons per layer & 32 \\ \cline{1-1}  & Activation function & ReLU \\ \cline{1-1}  & Optimizer & SGD \\ \hline \multirow{4}{*}{NSGA-II} & Combination probability & 0.94 \\ \cline{1-1}  & Mutation probability & 0.08 \\ \cline{1-1}  & Population size & 100 \\ \cline{1-1}  & Iteration & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimized hyperparameter settings for the proposed methods using grid search. process, requiring significantly more epochs to reduce their error rates. Their final RMSE values remain considerably higher, indicating that while these architectures capture useful patterns, they struggle to generalize SEE trade-offs as effectively as the hybrid CNN-LSTM framework. The standalone CNN and LSTM models exhibit the weakest convergence performance, with high initial RMSE values and slow reduction over epochs.