Constrained multi-fidelity Bayesian optimization with automatic stop condition
This addresses cost and feasibility issues in Bayesian optimization for critical applications, but it is incremental as it builds on existing multi-fidelity and constrained methods.
The paper tackled the high costs and lack of systematic stopping in Bayesian optimization for constrained search spaces by developing a constrained cost-aware multi-fidelity framework, achieving validation on multiple benchmarks with publicly available code.
Bayesian optimization (BO) is increasingly employed in critical applications to find the optimal design with minimal cost. While BO is known for its sample efficiency, relying solely on costly high-fidelity data can still result in high costs. This is especially the case in constrained search spaces where BO must not only optimize but also ensure feasibility. A related issue in the BO literature is the lack of a systematic stopping criterion. To solve these challenges, we develop a constrained cost-aware multi-fidelity BO (CMFBO) framework whose goal is to minimize overall sampling costs by utilizing inexpensive low-fidelity sources while ensuring feasibility. In our case, the constraints can change across the data sources and may be even black-box functions. We also introduce a systematic stopping criterion that addresses the long-lasting issue associated with BO's convergence assessment. Our framework is publicly available on GitHub through the GP+ Python package and herein we validate it's efficacy on multiple benchmark problems.