One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.

翻译：缩短优化研究时间的一种方法是并行评估设计方案，而非逐一进行。针对高评估代价的黑箱问题，已提出批处理版本的贝叶斯优化。该方法通过构建黑箱的代理模型，利用填充准则同时选择多个设计方案。然而，尽管计算资源的日益丰富使得大规模并行成为可能，当前适用于选择数十个并行方案评估的策略仍因选择更多方案时的复杂性而受限。当黑箱存在噪声时，这一问题尤为突出，因为需要更多评估次数及重复实验。本文提出一种可原生支持大规模批处理的可扩展策略，聚焦于探索/利用权衡与组合分配。我们在带噪声函数上将该方法与相关方法进行对比，涵盖单目标和多目标优化任务。实验表明，与现有方法相比，该方法在性能相当或更优的前提下，实现了数量级的加速。