论文标题
Max-value熵搜索具有约束的多目标贝叶斯优化
Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints
论文作者
论文摘要
我们考虑使用昂贵的功能评估的约束多目标黑框优化的问题，在这种问题中，目标是近似满足一组约束的真正帕累托解决方案集，同时最大程度地减少功能评估的数量。例如，在航空电力系统设计应用中，我们需要找到权衡总能量和质量的设计，同时满足电动机温度和电压电压的特定阈值。这种优化需要进行昂贵的计算模拟来评估设计。在本文中，我们提出了一种新方法，称为{\ em max-value熵搜索，搜索具有约束（mesmoc）}的多目标优化，以解决此问题。 MESMOC采用基于输出空间熵的采集功能来有效地选择输入序列以评估以发现高质量的帕累托解决方案，同时满足约束。 我们将MESMOC应用于两个现实世界的工程设计应用程序，以证明其对最新算法的有效性。
We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of function evaluations. For example, in aviation power system design applications, we need to find the designs that trade-off total energy and the mass while satisfying specific thresholds for motor temperature and voltage of cells. This optimization requires performing expensive computational simulations to evaluate designs. In this paper, we propose a new approach referred as {\em Max-value Entropy Search for Multi-objective Optimization with Constraints (MESMOC)} to solve this problem. MESMOC employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation to uncover high-quality pareto-set solutions while satisfying constraints. We apply MESMOC to two real-world engineering design applications to demonstrate its effectiveness over state-of-the-art algorithms.
