This paper addresses the problem of constrained multi-objective optimization over black-box objective functions with practitioner-specified preferences over the objectives when a large fraction of the input space is infeasible (i.e., violates constraints). This problem arises in many engineering design problems including analog circuits and electric power system design. Our overall goal is to approximate the optimal Pareto set over the small fraction of feasible input designs. The key challenges include the huge size of the design space, multiple objectives and large number of constraints, and the small fraction of feasible input designs which can be identified only after performing expensive simulations. We propose a novel and efficient preference-aware constrained multi-objective Bayesian optimization approach referred to as PAC-MOO to address these challenges. The key idea is to learn surrogate models for both output objectives and constraints, and select the candidate input for evaluation in each iteration that maximizes the information gained about the optimal constrained Pareto front while factoring in the preferences over objectives. Our experiments on two real-world analog circuit design optimization problems demonstrate the efficacy of PAC-MOO over prior methods.

翻译：本文针对黑箱目标函数下的约束多目标优化问题展开研究，其中实践者需在大部分输入空间不可行（即违反约束）的情况下指定对目标的偏好。该问题广泛存在于模拟电路和电力系统设计等工程优化领域。我们的总体目标是在极小可行输入设计空间中逼近最优帕累托集。核心挑战包括：设计空间规模巨大、多目标与多约束并存、以及需通过昂贵仿真才能识别的极小可行输入设计比例。我们提出一种新颖高效的偏好感知约束多目标贝叶斯优化方法PAC-MOO来应对这些挑战。其核心思想是：同时学习输出目标与约束的代理模型，并在每轮迭代中选择能最大程度获取最优约束帕累托前沿信息且兼顾目标偏好的候选输入进行评估。针对两个真实模拟电路设计优化问题的实验表明，PAC-MOO相较于现有方法具有显著优越性。