用迁移学习解决飞机设计优化的冷启动难题，提升早期收敛速度。
Transfer Learning in Bayesian Optimization for Aircraft Design
- 构建基于迁移学习的代理模型集成框架，融合多源数据
- 在早期迭代中实现更快收敛，目标与约束预测更准确
- 适合复杂工程优化场景，尤其飞机概念设计领域
在贝叶斯优化中引入迁移学习，可缓解所谓‘冷启动’问题，通过源数据辅助目标问题的优化。本文提出一种基于迁移学习的代理模型集成方法，并嵌入约束贝叶斯优化框架。针对飞机设计优化中存在的异质设计变量和约束问题，提出采用偏最小二乘法进行维度降维以应对设计空间异质性，以及采用‘元’数据代理选择方法处理约束异质性。通过数值基准测试和飞机概念设计优化问题验证所提方法。结果表明，在早期优化迭代中收敛性能显著提升，目标函数与约束代理模型的预测精度均有改善。
原文摘要 · Abstract (English)
The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \textit{cold start} problem by using source data to aid in the optimization of a target problem. We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework. We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints. We propose the use of a partial-least-squares dimension reduction algorithm to address design space heterogeneity, and a \textit{meta} data surrogate selection method to address constraint heterogeneity. Numerical benchmark problems and an aircraft conceptual design optimization problem are used to demonstrate the proposed methods. Results show significant improvement in convergence in early optimization iterations compared to standard Bayesian optimization, with improved prediction accuracy for both objective and constraint surrogate models.
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