# Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design

arXiv: https://arxiv.org/abs/2005.05067 · HTML: https://mlsanity.com/paper/2005.05067/

## Key facts

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

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

- [Multi-fidelity Bayesian Optimization: A Review](https://mlsanity.com/paper/2311.13050/) (2023)
- [High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft](https://mlsanity.com/paper/2311.06130/) (2023)
- [Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design](https://mlsanity.com/paper/2504.08682/) (2025)
- [No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations](https://mlsanity.com/paper/2305.03824/) (2023)
- [Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design](https://mlsanity.com/paper/2504.08671/) (2025)
- [CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints](https://mlsanity.com/paper/2211.11822/) (2022)
- [Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction](https://mlsanity.com/paper/2503.19479/) (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)
- [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)

## Abstract (arXiv)

Bayesian optimization methods have been successfully applied to black box optimization problems that are expensive to evaluate. In this paper, we adapt the so-called super effcient global optimization algorithm to solve more accurately mixed constrained problems. The proposed approach handles constraints by means of upper trust bound, the latter encourages exploration of the feasible domain by combining the mean prediction and the associated uncertainty function given by the Gaussian processes. On top of that, a refinement procedure, based on a learning rate criterion, is introduced to enhance the exploitation and exploration trade-off. We show the good potential of the approach on a set of numerical experiments. Finally, we present an application to conceptual aircraft configuration upon which we show the superiority of the proposed approach compared to a set of the state-of-the-art black box optimization solvers. Keywords: Global Optimization, Mixed Constrained Optimization, Black box optimization, Bayesian Optimization, Gaussian Process.