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:本文针对黑盒目标函数上的约束多目标优化问题,其中大部分的输入空间是不可行的(即违反了约束条件),同时使用实践者指定的目标偏好。这个问题出现在许多工程设计问题,包括模拟电路和电力系统设计。我们的总体目标是在可行的输入设计的小部分上逼近最优帕累托集。主要的挑战包括设计空间的巨大大小、多个目标和大量的约束条件,以及只有在执行昂贵的模拟后才能识别出的可行输入设计的小部分。我们提出了一种新颖有效的优先考虑目标偏好的约束多目标贝叶斯优化方法,称为PAC-MOO,来解决这些挑战。关键思想是学习输出目标和约束条件的模型,并在每次迭代中选择最大化关于最优约束帕累托前沿的信息收益的候选输入,同时考虑到目标偏好。我们在两个实际的模拟电路设计优化问题上进行了实验,证明了PAC-MOO相对于之前的方法的有效性。