Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization
CoRR(2023)
摘要
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.
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