## 3 Example Applications and Results
### Application of SMPO
The GA hyperparameters are set as given in Table 4.0 The coding length of genetic individuals is equal to the number of parameters to be optimized (i.e., 11), and each individual coding contains all the information of the parameters to be optimized. The size of the initialized population is 100. The individuals are input to the surrogate model to calculate the fitness value, and the poorly adapted individuals are eliminated in the manner of survival of the fittest. Meanwhile, the highly adapted individuals are crossed and mutated to produce new individuals. New rounds of iterations are performed until the best adapted individuals in the population are selected. One-hundred iterations are performed. The iterative process is presented in Table 4.1 The algorithm selects the best individual as having a fitness value of \(-0.0002\), indicating that the difference between the temperature of the optimized CMOS and the CMOS index is not greater than 0.0002. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hyperparameters** & **Detail** & **Value** \\ \hline
1 & PopSize & Population size & 100 \\
2 & MaxTerm & Maximum iterations & 100 \\
3 & Method of Mutation & mutation & boundaryMutation \\
4 & Method of Select & Select & normGeomSelect \\
5 & Method of Crossover & Crossover & heuristicXover \\ \hline \hline \end{tabular}
\end{table} 
Table 6: GA hyperparameter settings. Figure 10: Prediction errors of different networks. To verify the optimized results, the parameters optimized by the GA are substituted into software for Monte Carlo simulation using the finite element method for thermal design, and the optimized results are verified for the high- and low-temperature cases of the telescope. The verification results are presented in Figures 11-13. Figure 11a shows the temperature cloud of the CMOS heat dissipation component in the low-temperature case whereas Figure 11b shows the temperature cloud of the CMOS itself in the low-temperature case. Figure 12a shows the temperature cloud of the CMOS heat dissipation component in the high-temperature case whereas Figure 12b shows the temperature cloud of the CMOS itself in the high-temperature case. Figure 13 shows the temperature fluctuation of the CMOS itself in the high- and low-temperature cases. The figures reveal that in the low-temperature case, as the internal heat source essentially fails to work and the CMOS package in the cabin is less affected by external heat flow, the overall minimum temperature of the CMOS components exceeds 10 \({}^{\circ}\)C, the maximum temperature is less than 13.9 \({}^{\circ}\)C, the temperature of the CMOS itself is stable at 12.82 \({}^{\circ}\)C, and the temperature uniformity is within 0.01 \({}^{\circ}\)C. In the high-temperature case, the internal heat source works intermittently, the overall temperature uniformity of the CMOS components is within 0.93 \({}^{\circ}\)C, the minimum temperature of the CMOS itself is 31.10 \({}^{\circ}\)C, the maximum temperature is 32.69 \({}^{\circ}\)C, and the maximum temperature fluctuation is less than 1.6 \({}^{\circ}\)C, which meets the demands of the CMOS thermal control index.