# An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design

arXiv: https://arxiv.org/abs/2006.08434 · HTML: https://mlsanity.com/paper/2006.08434/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal](https://mlsanity.com/paper/2303.01560/) (2023)
- [Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design](https://mlsanity.com/paper/2504.08682/) (2025)
- [Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design](https://mlsanity.com/paper/2504.08671/) (2025)
- [Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications](https://mlsanity.com/paper/2504.09930/) (2025)
- [High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings](https://mlsanity.com/paper/2502.00854/) (2025)
- [System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility](https://mlsanity.com/paper/2507.08715/) (2025)
- [Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design](https://mlsanity.com/paper/2603.28987/) (2026)
- [Transfer Learning in Bayesian Optimization for Aircraft Design](https://mlsanity.com/paper/2603.28999/) (2026)
- [Efficient multidisciplinary design via Bayesian optimization](https://mlsanity.com/paper/2607.22560/) (2026)
- [Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systems](https://mlsanity.com/paper/2609.22130/) (2026)
- [STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization](https://mlsanity.com/paper/2610.14338/) (2026)

## Abstract (arXiv)

The multi-level, multi-disciplinary and multi-fidelity optimization framework developed at Bombardier Aviation has shown great results to explore efficient and competitive aircraft configurations. This optimization framework has been developed within the Isight software, the latter offers a set of ready-to-use optimizers. Unfortunately, the computational effort required by the Isight optimizers can be prohibitive with respect to the requirements of an industrial context. In this paper, a constrained Bayesian optimization optimizer, namely the super efficient global optimization with mixture of experts, is used to reduce the optimization computational effort. The obtained results showed significant improvements compared to two of the popular Isight optimizers. The capabilities of the tested constrained Bayesian optimization solver are demonstrated on Bombardier research aircraft configuration study cases.