论文标题
批处理多目标贝叶斯优化的罚款方法，并在热交换器设计中应用
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
论文作者
论文摘要
我们提出了高度可行的帕累托优化（HIPPO） - 批处理采集功能，可实现多目标贝叶斯优化方法，以有效利用并行处理资源。多目标贝叶斯优化（MOBO）是解决昂贵的黑盒问题的非常有效的工具。但是，大多数主板算法被设计为纯粹的顺序策略，而现有的批处理方法对于除最小的批量尺寸以外的所有人都非常昂贵。我们表明，通过通过以相似的预测目标值进行惩罚评估来鼓励批处理多样性，Hippo可以便宜地建立大量的信息观点。我们广泛的实验验证表明，河马至少与现有替代方案一样有效，同时产生的计算机开销较低，并易于扩展到比文献中当前支持的批次大小要高得多。此外，我们证明了河马在充满挑战的热交换器设计问题上的应用，这强调了我们高度可行的主板方法的现实效用。
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources. Multi-Objective Bayesian Optimisation (MOBO) is a very efficient tool for tackling expensive black-box problems. However, most MOBO algorithms are designed as purely sequential strategies, and existing batch approaches are prohibitively expensive for all but the smallest of batch sizes. We show that by encouraging batch diversity through penalising evaluations with similar predicted objective values, HIPPO is able to cheaply build large batches of informative points. Our extensive experimental validation demonstrates that HIPPO is at least as efficient as existing alternatives whilst incurring an order of magnitude lower computational overhead and scaling easily to batch sizes considerably higher than currently supported in the literature. Additionally, we demonstrate the application of HIPPO to a challenging heat exchanger design problem, stressing the real-world utility of our highly parallelisable approach to MOBO.
