Trust regions combined with penalty-based constraints enable Bayesian optimization to find feasible solutions faster in high-dimensional constrained problems where evaluations are expensive.
This paper presents a Bayesian optimization method for expensive black-box optimization problems with constraints. It combines penalty-based constraint handling, surrogate modeling, and trust regions to efficiently find good solutions in high dimensions with fewer evaluations.