Preference-Aware Constrained Multi-Objective Bayesian Optimization

Abstract

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. We aim to approximate the optimal Pareto set over the small fraction of feasible input designs. The key challenges include the massive size of the design space, multiple objectives, a large number of constraints, and the small fraction of feasible input designs, which can be identified only after performing expensive experiments/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 synthetic and challenging real-world analog circuit design optimization problems demonstrate the efficacy of PAC-MOO over baseline methods.