We introduce an algorithm for the optimization of problems whose objective functions are evaluated by computationally expensive black-box simulations and for which an analytic description of the objective and its derivatives are not available. We consider the case where two levels of simulation model fidelity are available, namely a high fidelity model that is computationally very expensive to evaluate, and a low fidelity model that is less accurate and computationally cheaper but still time consuming. The computational effort is alleviated by using computationally cheap surrogate models that approximate the simulations at both fidelity levels. The local correlation between both fidelity surrogate models determines when the low fidelity model can be trusted for making sampling decisions for the high fidelity model. In the numerical experiments we investigate how well our algorithm responds to problems whose objective function fidelity levels are well correlated and badly correlated. We study how different initial design strategies and parameter settings impact the performance of the algorithm. The results show that our algorithm actively learns from the local correlation computations how well suited the low fidelity model is for making sampling decisions and it ignores the low fidelity model if the correlation is too low.

我们介绍了一种用于优化问题的算法，该算法的目标函数是通过计算量大的黑盒模拟评估的，而该目标及其派生子的解析描述不可用。我们考虑了两种级别的仿真模型保真度可用的情况，即高保真度模型的评估在计算上非常昂贵，而低保真度模型的准确性较差并且在计算上更便宜但仍很耗时。通过使用在两个逼真度级别都近似仿真的廉价计算代理模型，可以减轻计算工作量。两个保真度替代模型之间的局部相关性决定了何时可以信任低保真度模型来为高保真度模型做出决策。在数值实验中，我们研究了算法对目标函数保真度水平相关性和相关性差的问题的响应能力。我们研究了不同的初始设计策略和参数设置如何影响算法的性能。结果表明，我们的算法积极地从局部相关性计算中学习了低保真度模型在进行抽样决策方面的适用性，如果相关度太低，则它会忽略低保真度模型。