Feasibility-Driven Trust Region Bayesian Optimization

arXiv (Cornell University)

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 and other physics-inspired benchmarks, 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 60.

Authors 4

1. Paolo Ascia

   Technical University of Munich

   Affiliation as printed

   Technical University of Munich , Arcisstr. 21 , 80333 Munich , Germany

2. Elena Raponi Aachen

   Leiden University

   Affiliation as printed

   LIACS , Leiden University , Einsteinweg 55 , 2333 CC Leiden , The Netherlands

3. Thomas Bäck Aachen

   Leiden University

   Affiliation as printed

   LIACS , Leiden University , Einsteinweg 55 , 2333 CC Leiden , The Netherlands

4. Fabian M. E. Duddeck

   Technical University of Munich

   Affiliation as printed

   Technical University of Munich , Arcisstr. 21 , 80333 Munich , Germany