## 5 Rotor Temperature Modeling and Prediction
### In-Depth Discussion on Model Performance  
In both experiments, RIME-XGBoost, SMA-RF, SO-BiGRU, and EO-SVR optimized their hyperparameters using different optimization algorithms. RIME-XGBoost accurately learned the relationship between input features and rotor temperature in both medium-scale and small-scale samples, making it the best-performing model overall. A deeper discussion from the perspectives of the base model and optimization algorithm reveals the reasons for its excellent prediction performance.  
Figure 9: Spearman correlation between features and rotor temperature in Group B.  
RIME-XGBoost uses RIME to optimize the number of trees, tree depth, and learning rate of XGBoost. XGBoost, an ensemble learning method, excels at establishing complex nonlinear relationships, often resulting in high training set fitting. In addition, XGBoost incorporates regularization terms, effectively preventing overfitting.  
RIME efficiently finds the optimal hyperparameter combination through a combination of global search and local optimization, further enhancing the model's learning and generalization capabilities. The ensemble learning mechanism, hyperparameter tuning, and built-in regularization collectively contribute to RIME-XGBoost's prediction performance.  
In comparison, SMA-RF performs suboptimally but has high computational efficiency. RF, based on Bagging ensemble learning, has strong anti-overfitting capabilities and can be trained in parallel. It is suitable for medium- to large-scale data. However, as the number of decision trees and the minimum leaf size increase, computational complexity rises significantly. SMA optimizes two hyperparameters, which enhances SMA-RF's overall capabilities.  
SO-BiGRU's weakness lies in the instability of its prediction results in each round, along with the highest computational complexity. Although BiGRU can learn long-term dependencies in sequential data, recurrent neural networks are highly sensitive to hyperparameter choices. An insufficient hyperparameter search can lead to poor prediction performance. Furthermore, the complex structure of BiGRU and excessive population and iteration choices in the optimization algorithm result in exceptionally long Runtimes per round. This tradeoff between prediction performance and computational efficiency limits its applicability.  
Finally, EO-SVR shows moderate performance but limited prediction accuracy. SVR can learn nonlinear relationships in data and is robust to outliers. The main limitation of EO-SVR is its relatively low prediction accuracy. In other words, while EO efficiently searches the hyperparameter space, the SVR hyperparameter space may contain multiple local optima, preventing the optimization result from being globally optimal. Hyperparameters tuned by EO enhance SVR's performance. However, SVR's sensitivity to hyperparameters still restricts further performance improvements. Increasing the population and iterations of the optimization algorithm might address this issue.  
Therefore, selecting RIME-XGBoost for building a rotor temperature prediction model is the optimal solution. The ensemble learning mechanism, collaborative optimization of multiple hyperparameters, and regularization settings collectively enhance its prediction accuracy and generalization ability.