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This is a Quantitative Study study (also classified as: Experimental Study).

October 13, 2025Open Access

Constrained multi-fidelity Bayesian optimization with automatic stop condition

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This framework minimizes sampling costs while ensuring feasibility in constrained searches, highlighting improved efficiency.

The constrained multi-fidelity Bayesian optimization framework minimizes overall sampling costs while maintaining feasibility.
A systematic stopping criterion was developed to resolve the long-standing issue of convergence assessment in Bayesian optimization.
The framework effectively utilizes low-fidelity data sources to reduce costs associated with high-fidelity data while ensuring compliance with constraints.
The applied methods were validated across multiple benchmark problems, demonstrating strong efficacy in constrained optimization scenarios.

Foumani et al. (2025) studied this question.

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