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NM2-BO: Non-Myopic Multifidelity Bayesian Optimization
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DOI：10.1016/j.knosys.2024.111959.png)
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摘要
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Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allow to accelerate Bayesian optimization by exploiting low -fidelity representations of expensive objective functions. Popular multifidelity Bayesian strategies rely on sampling policies that account for the immediate reward obtained evaluating the objective function for a specific input, precluding greater informative gains that might be obtained looking ahead more steps. This paper proposes a Non -Myopic Multifidelity Bayesian Optimization framework (NM2-BO) to grasp the long-term reward from future steps of the optimization. Our computational strategy comes with a two-step lookahead multifidelity acquisition function that maximizes the cumulative reward obtained measuring the improvement of the solution over two steps ahead. Our NM2-BO algorithm is demonstrated for a large set of benchmark problems to stress test across a broad spectrum of mathematical properties, and a physics -based application relevant for the engineering community. In all the cases, we observe that the proposed algorithm outperforms standard MFBO frameworks.
Keyword:
Non-myopic multifidelity method
Multifidelity Bayesian optimization
Two-step lookahead multifidelity acquisition
function
Multifidelity Gaussian process
Engineering optimization
Simulation-based optimization
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IF:
7.6
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1.3W
被引数:
4.5W
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