## 5 Discussion
### Comparison with Other Prediction Models  
In order to further verify the superiority of the prediction performance of the IAJS-HKRVM model, SVM, backpropagating neural network (BPNN), and the AJS-HKRVM model are selected to predict the ventilation local resistance coefficient, respectively, and are compared with the prediction results of the IAJS-HKRVM model. Among them, the radial basis kernel function is used in the SVM model, and the kernel parameters and penalty factor are determined by grid search. The learning rate of the BPNN is 0.001, and the hyperparameters are determined by Bayesian optimization. The boxplot of the residual error of the prediction results of each model is shown in Figure 13, the prediction error index comparison of each model is shown in Table 5, and the prediction error index improvement rate of the IAJS-HKRVM model compared with other models is shown in Figure 14.  
As shown in Figure 13, the maximum and minimum values of the prediction residual error of the IAJS-HKRVM model are lower than those of the other prediction models, and its prediction residual error distribution is the most concentrated, and the median is the closest to 0. The residual error distribution concentration of the AJS-HKRVM model is close to that of the IAJS-HKRVM model, but its maximum residual is slightly larger. The SVM has the second highest degree of prediction residual error distribution concentration, while the BPNN has the lowest degree of prediction residual error distribution concentration, which is consistent with the prediction error indicators of each model in Table 5. As can be seen from Table 5, the error indexes of the IAJS-HKRVM model are better than those of the AJS-HKRVM, SVM, and BPNN. As can be seen from Figure 14, the numbers marked in red in Figure 14 show the highest improvement rate of each error index of the IAJS-HKRVM model compared with other conventional prediction models. The \(R^{2}\) of the IAJS-HKRVM model has the highest increase of 18.8% compared with the BPNN, and the lowest increase  
Figure 13: Boxplot of different model prediction residual error. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & \(\mathbf{MAE}\) & \(\mathbf{RMSE}\) \\ \hline SVM & 0.8505 & 0.0050 & 0.0060 \\ BPNN & 0.8339 & 0.0051 & 0.0063 \\ AJS-HKRVM & 0.9857 & 0.0014 & 0.0019 \\
**IAJS-HKRVM** & **0.9903** & **0.0013** & **0.0015** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of other model prediction error indexes. of 0.5% compared with the AJS-HKRVM model. The _MAE_ has the largest decrease of 75.2% compared with the BPNN, and lowest decrease of 11.6% compared with the AJS-HKRVM model. The _RMSE_ has the largest decrease of 75.8% compared with the BPNN, and the lowest decrease of 17.7% compared with the AJS-HKRVM model. Therefore, the prediction performance of the IAJS-HKRVM model has been greatly improved compared with other conventional prediction models. As shown in Figures 13 and 14, the prediction accuracy of the IAJS-HKRVM model is only slightly improved compared with the AJS-HKRVM model. Figure 15 shows the comparison of the convergence curves of the AJS-HKRVM and IAJS-HKRVM model. As can be seen from Figure 15, during the first iteration, the average optimal fitness values of the AJS-HKRVM and IAJS-HKRVM model are 0.0424 and 0.0308, respectively. In addition, by the 38 th iteration of the IAJS-HKRVM, the jellyfish population has been concentrated in the region near the optimal solution, while the AJS-HKRVM arrived at the region near the optimal solution at the 46 th iteration. The running time of each iteration is the same, so the optimization efficiency of the IAJS-HKRVM model is improved by about 17.4% compared with the AJS-HKRVM.