标题： 为批量贝叶斯优化选择合适的获取函数：序列方法与蒙特卡洛方法的比较 显示英文标题

标题： Choosing a Suitable Acquisition Function for Batch Bayesian Optimization: Comparison of Serial and Monte Carlo Approaches

摘要： 批量贝叶斯优化广泛用于优化昂贵的实验过程，尤其是在可以同时测试多个样本以节省时间和成本的情况下。 在设计贝叶斯优化活动以指导实验时的一个关键决策是在几乎不了解“黑箱”函数景观的情况下选择批量获取函数。 为了帮助做出这一决策，我们首先比较了两种数学函数上串行和蒙特卡罗批量获取函数的性能，这两种函数作为典型材料合成和加工实验的代理。 这两个函数均为六维，分别是体现“针 haystack 搜索”的Ackley函数和体现“虚假最优值”问题的Hartmann函数。 我们的研究评估了串行置信上限与局部惩罚（UCB/LP）批量获取策略与基于蒙特卡罗的并行方法：q-log预期改进（qlogEI）和q-置信上限（qUCB），其中q是批量大小。 Ackley和Hartmann上的测试表明，在无噪声条件下，UCB/LP和qUCB表现良好，两者都优于qlogEI。 对于有噪声的Hartmann函数，所有蒙特卡罗函数相对于UCB/LP在收敛速度更快且对初始条件的敏感性更低。 然后，我们在从实验数据构建的经验回归模型上验证了在最大化柔性钙钛矿太阳能电池功率转换效率方面的发现。 我们的结果表明，当在小于或等于六个维度上经验性地优化一个“黑箱”函数且对景观或噪声特性没有先验知识时，qUCB最适合作为默认选项，以最大化对模型最优值的信心，同时最小化所需昂贵样本的数量。 显示英文摘要

摘要： Batch Bayesian optimization is widely used for optimizing expensive experimental processes when several samples can be tested together to save time or cost. A central decision in designing a Bayesian optimization campaign to guide experiments is the choice of a batch acquisition function when little or nothing is known about the landscape of the "black box" function to be optimized. To inform this decision, we first compare the performance of serial and Monte Carlo batch acquisition functions on two mathematical functions that serve as proxies for typical materials synthesis and processing experiments. The two functions, both in six dimensions, are the Ackley function, which epitomizes a "needle-in-haystack" search, and the Hartmann function, which exemplifies a "false optimum" problem. Our study evaluates the serial upper confidence bound with local penalization (UCB/LP) batch acquisition policy against Monte Carlo-based parallel approaches: q-log expected improvement (qlogEI) and q-upper confidence bound (qUCB), where q is the batch size. Tests on Ackley and Hartmann show that UCB/LP and qUCB perform well in noiseless conditions, both outperforming qlogEI. For the Hartmann function with noise, all Monte Carlo functions achieve faster convergence with less sensitivity to initial conditions compared to UCB/LP. We then confirm the findings on an empirical regression model built from experimental data in maximizing power conversion efficiency of flexible perovskite solar cells. Our results suggest that when empirically optimizing a "black-box" function in less than or equal to six dimensions with no prior knowledge of the landscape or noise characteristics, qUCB is best suited as the default to maximize confidence in the modeled optimum while minimizing the number of expensive samples needed.