mlsanityData to 14 Oct 2026

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

arXiv 2504.08682 ↗First posted 11 Apr 2025stat.ME cs.LG math.OCcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 17 of its 62 references that are in the map. First posted on arXiv 11 Apr 2025.

Builds on

17 of 62 references.

References · 17

  1. 2020pymoo: Multi-objective Optimization in Pythoncited by 200
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  12. 2020High-Dimensional Bayesian Optimization via Nested Riemannian Manifoldscited by 17
5 more
  1. 2018Efficient global optimization of constrained mixed variable problemscited by 11
  2. 2020Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Designcited by 11
  3. 2020An efficient application of Bayesian optimization to an industrial MDO framework for aircraft designcited by 11
  4. 2021Revisiting Bayesian Optimization in the light of the COCO benchmarkcited by 10
  5. 2020Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problemscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Designcited by 4
  2. 2025System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobilitycited by 1
  3. 2025A reproducible comparative study of categorical kernels for Gaussian process regression, with new clustering-based nested kernelscited by 0
  4. 2026Transfer Learning in Bayesian Optimization for Aircraft Designcited by 0
  5. 2026Efficient multidisciplinary design via Bayesian optimizationcited by 0
  6. 2026Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systemscited by 0
  7. 2026Energy-aware frugal Bayesian optimizationcited by 0