Feasibility-Driven Trust Region Bayesian Optimization

TL;DR

This paper introduces FuRBO, a trust region Bayesian optimization method that adaptively refocuses search efforts to efficiently find feasible solutions in constrained, high-dimensional black-box optimization problems.

Contribution

FuRBO is a novel algorithm that dynamically adjusts trust regions based on constraint and objective models, improving feasibility discovery and optimization efficiency.

Findings

FuRBO outperforms state-of-the-art methods on BBOB-constrained COCO benchmarks.

It effectively handles high-dimensional problems up to 60 dimensions.

The adaptive trust region strategy accelerates feasible solution discovery.

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.