Feasibility-Driven Trust Region Bayesian Optimization

Paolo Ascia, Elena Raponi, Thomas Bäck, Duddeck

Confirmation: our paper adheres to reproducibility best practices. In particular, we confirm that all important details required to reproduce results are described in the paper,, the authors agree to the paper being made available online through OpenReview under a CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0/), and, the authors have read and commit to adhering to the AutoML 2025 Code of Conduct (https://2025.automl.cc/code-of-conduct/).

TL;DR: We present FuRBO, a Feasibility-Driven Trust Region Bayesian Optimization algorithm for severely constrained scenarios, which iteratively selects candidate solutions from a trust region informed by both objective and constraint surrogate models.

Abstract: Bayesian optimization is a powerful tool for solving real-world optimization tasks under tight evaluation budgets, making it well-suited for applications involving costly simulations or experiments. However, many of these tasks are also characterized by the presence of expensive constraints whose analytical formulation is unknown and often defined in high-dimensional spaces where feasible regions are small, irregular, and difficult to identify. In such cases, a substantial portion of the optimization budget may be spent just trying to locate the first feasible solution, limiting the effectiveness of existing methods. In this work, we present a Feasibility-Driven Trust Region Bayesian Optimization (FuRBO) algorithm. FuRBO iteratively defines a trust region from which the next candidate solution is selected, using information from both the objective and constraint surrogate models. Our adaptive strategy allows the trust region to shift and resize significantly between iterations, enabling the optimizer to rapidly refocus its search and consistently accelerate the discovery of feasible and good-quality solutions. We empirically demonstrate the effectiveness of FuRBO through extensive testing on the full BBOB-constrained COCO benchmark suite, comparing it against state-of-the-art baselines for constrained black-box optimization across varying levels of constraint severity and problem dimensionalities ranging from 2 to 40.

Submission Number: 46