## 4 Conclusions  
This paper proposes a surrogate model-based method for optimizing the thermal parameters of space telescopes, called SMPO. The method employs a BP neural network surrogate model based on the adaptive learning rate (called GAALBP) so as to bear a lower computational cost than the traditional thermal design approach of solving partial differential equations. Additionally, the proposed method uses a genetic algorithm (GA) to optimize the weights and thresholds of the BP network and thus improves the accuracy of the surrogate model. After the surrogate model is established, the genetic algorithm is used again to optimize the input of the network so that the output of the network approximates the target value.  
In this paper, we established a thermophysical model of a space telescope (called the ADST), selected 11 parameters of the heat dissipation path of the CMOS detector as indicators to be optimized, applied the GAALBP network surrogate model to approximate the thermophysical model of the ADST, established the mapping relationship between the 11 indicators to be optimized and the temperature of the CMOS, and used a GA to optimize indicators ensuring the output meets CMOS temperature requirements. The theoretical and simulation results reveal that SMPO proposed in the paper outperforms traditional engineer-dependent optimization, in terms of the model evaluation accuracy and higher computational efficiency.  
Space telescopes are developing toward the direction of modularization, rapid launch, and short design cycles. Optimization design methods such as those similar to SMPO that can quickly realize multi-parameter intelligence and automation is of particular importance. Moreover, SMPO is an optimization framework and an optimization idea. The rapid optimization process can be transplanted into other models to achieve rapid thermal design and batch implementation. SMPO is applicable to not only the optimization of the thermal design parameters of space telescopes but also post-processing and design optimization in other fields. However, the convergence of SMPO is not particularly stable, and the SMPO optimization framework does not automate the post-processing of data, with manual data conversion still a requirement. Therefore, it remains necessary to further improve the convergence and stability of the SMPO, and to achieve full automation of SMPO.  
Figure 14: Comparison of the performance of parameter optimization in high- and low-temperature cases.  
**Author Contributions:** Conceptualization, W.Z. and L.G.; methodology, W.Z.; software, W.Z.; validation, Y.X., D.T. and L.G.; formal analysis, W.Z.; investigation, W.Z.; resources, L.G.; data curation, Z.J.; writing--original draft preparation, W.Z.; writing--review and editing, L.G.; visualization, W.Z.; supervision, Z.J.; project administration, L.G.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61605203 and the Youth Innovation Promotion Association of the Chinese Academy of Sciences, grant number 2015173.  
Not applicable.  
This work was supported by the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. We thank [PERSON] (Edanz) (www.liwenbianji.cn; accessed on 7 January 2022) for editing the language of a draft of this manuscript.  
The authors declare no conflict of interest.