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。