Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917982663344128 |
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| author | Saves, Paul Bartoli, Nathalie Diouane, Youssef Lefebvre, Thierry Morlier, Joseph David, Christophe Van, Eric Nguyen Defoort, Sébastien |
| author_facet | Saves, Paul Bartoli, Nathalie Diouane, Youssef Lefebvre, Thierry Morlier, Joseph David, Christophe Van, Eric Nguyen Defoort, Sébastien |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08682 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design Saves, Paul Bartoli, Nathalie Diouane, Youssef Lefebvre, Thierry Morlier, Joseph David, Christophe Van, Eric Nguyen Defoort, Sébastien Methodology Machine Learning Optimization and Control 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. |
| title | Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design |
| topic | Methodology Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2504.08682 |