We consider the problem of multi-objective (MO) blackbox optimization using
expensive function evaluations, where the goal is to approximate the true
Pareto set of solutions while minimizing the number of function evaluations.
For example, in hardware design optimization, we need to find the designs that
trade-off performance, energy, and area overhead using expensive simulations.
We propose a novel uncertainty-aware search framework referred to as USeMO to
efficiently select the sequence of inputs for evaluation to solve this problem.
The selection method of USeMO consists of solving a cheap MO optimization
problem via surrogate models of the true functions to identify the most
promising candidates and picking the best candidate based on a measure of
uncertainty. We also provide theoretical analysis to characterize the efficacy
of our approach. Our experiments on several synthetic and six diverse
real-world benchmark problems show that USeMO consistently outperforms the
state-of-the-art algorithms.

本研究提出了一种新颖的不确定性感知搜索框架（USeMO），使用代理模型的多目标黑盒优化解决方案来选择评估输入序列，通过减少函数评估的数量，近似真正的 Pareto 集。我们在多个合成和六个不同的真实基准问题上的实验表明，USeMO 总是优于现有算法。

多目标贝叶斯优化不确定性感知搜索框架

Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization

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.

提出一种名为 MESMOC 的新方法来解决大规模设计问题的优化，通过使用输出空间熵的获得函数，选择评估输入序列来发现高质量的帕累托集解决方案同时满足约束条件。

带约束的多目标贝叶斯优化的最大值熵搜索

Max-value Entropy Search for Multi-Objective Bayesian Optimization with  Constraints

We consider the problem of constrained multi-objective (MO) 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. We propose a novel
framework named Uncertainty-aware Search framework for Multi-Objective
Optimization with Constraints (USeMOC) to efficiently select the sequence of
inputs for evaluation to solve this problem. The selection method of USeMOC
consists of solving a cheap constrained MO optimization problem via surrogate
models of the true functions to identify the most promising candidates and
picking the best candidate based on a measure of uncertainty. We applied this
framework to optimize the design of a multi-output switched-capacitor voltage
regulator via expensive simulations. Our experimental results show that USeMOC
is able to achieve more than 90 % reduction in the number of simulations needed
to uncover optimized circuits.

用代理模型进行不确定度感知的搜索，以在减小函数计算的前提下，近似求出满足一组约束条件的真实 Pareto 解集，将其应用于昂贵的电路优化实验中，实现了 90% 以上的计算次数减少。