用自适应降维减少混合变量贝叶斯优化的超参数，提升飞机设计效率。
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
- 基于偏最小二乘法实现超参数自适应缩减，降低模型复杂度。
- 在真实飞机设计任务中相较遗传算法提升显著，优化效率更高。
- 适合含连续、整数、类别变量的工程系统优化，如航空航天设计。
多学科设计优化旨在将数值优化技术应用于涉及多个学科的工程系统设计。在此背景下，优化过程中可能产生大量混合变量（连续、整数、类别），且实际应用常涉及众多设计变量。近年来，针对混合变量约束的贝叶斯优化受到关注，但现有方法严重增加代理模型相关的超参数数量。本文通过偏最小二乘法构建超参数更少的代理模型，并提出自适应选择超参数数量的流程。该方法在解析测试及两个真实的飞机设计应用中得到验证，相比遗传算法取得显著性能提升。
原文摘要 · Abstract (English)
Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of design variables. Recently, there has been a growing interest in mixed variables constrained Bayesian optimization but most existing approaches severely increase the number of the hyperparameters related to the surrogate model. In this paper, we address this issue by constructing surrogate models using less hyperparameters. The reduction process is based on the partial least squares method. An adaptive procedure for choosing the number of hyperparameters is proposed. The performance of the proposed approach is confirmed on analytical tests as well as two real applications related to aircraft design. A significant improvement is obtained compared to genetic algorithms.
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