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.

本研究提出了一种基于贝叶斯优化的 PAC-MOO 方法来解决在大部分输入空间不可行时（即违反约束条件），由实践者指定目标函数的约束多目标优化问题，特别地，在电路和电力系统设计等工程设计问题中。该方法能够学习输出目标和约束的代理模型，并选择在每次迭代中评估最大程度能获得有关最佳受约束 Pareto 前沿的信息且同时考虑目标偏好的候选输入，实验结果表明 PAC-MOO 比现有方法更加有效。