Uncertainty-Aware Offline Data-Driven Multi-Objective Optimization

TL;DR

This paper introduces a dual-ranking strategy for offline multi-objective optimization that effectively leverages surrogate models and uncertainty estimates to improve solution robustness.

Contribution

It proposes a flexible, uncertainty-aware ranking method that works with various surrogate models, addressing limitations of Gaussian Process Regression-based approaches.

Findings

The dual-ranking method improves solution robustness in offline MOO.

Experimental results show the method's effectiveness across different surrogate models.

The approach is scalable and computationally efficient compared to GPR-based methods.

Abstract

In offline data-driven multi-objective optimization (MOO), optimization is performed using surrogate models trained only on an offline dataset. These surrogate models contain inherent errors and uncertainty. This epistemic uncertainty can lead to incorrect dominance judgments, thereby misleading the search process. Existing methods mitigate this issue by incorporating uncertainty estimates from Gaussian Process Regression (GPR) to correct dominance judgments; however, they are restricted to GPR, and their optimization strategies cannot be scaled to other uncertainty quantification methods. In addition, GPR-based surrogates suffer from high computational cost. We propose a simple yet effective dual-ranking strategy that flexibly leverages both predictive results and uncertainty estimates from different surrogate models. By performing non-dominated sorting on candidate solutions using both surrogate-based fitness values and uncertainty-aware fitness values, the proposed method prioritizes candidate solutions that are simultaneously high-quality and reliable. Through extensive experimental evaluations, including ablation, sensitivity, and comparative experiments, we demonstrate the effectiveness and robustness of the proposed dual-ranking strategy working with different surrogates. Our dual-ranking framework offers more robust solutions for data-limited, real-world applications.