Pareto Set Learning (PSL) is a promising approach for approximating the
entire Pareto front in multi-objective optimization (MOO) problems. However,
existing derivative-free PSL methods are often unstable and inefficient,
especially for expensive black-box MOO problems where objective function
evaluations are costly. In this work, we propose to address the instability and
inefficiency of existing PSL methods with a novel controllable PSL method,
called Co-PSL. Particularly, Co-PSL consists of two stages: (1) warm-starting
Bayesian optimization to obtain quality Gaussian Processes priors and (2)
controllable Pareto set learning to accurately acquire a parametric mapping
from preferences to the corresponding Pareto solutions. The former is to help
stabilize the PSL process and reduce the number of expensive function
evaluations. The latter is to support real-time trade-off control between
conflicting objectives. Performances across synthesis and real-world MOO
problems showcase the effectiveness of our Co-PSL for expensive multi-objective
optimization tasks.

在这项工作中，我们提出了一种新颖的可控 Pareto 集学习 (Co-PSL) 方法，旨在解决现有无导数 PSL 方法在昂贵的黑盒多目标优化问题中不稳定和低效的问题，通过两个阶段的功能：(1) 使用贝叶斯优化进行预热，以获得高质量的高斯过程先验；(2) 可控 Pareto 集学习，准确获得从偏好到相应 Pareto 解的参数映射，以实现对冲突目标之间的实时权衡控制。综合合成和现实世界的多目标优化问题的性能展示了 Co-PSL 的有效性。