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.

翻译：在离线数据驱动的多目标优化中，优化过程仅依赖于通过离线数据集训练得到的代理模型。这些代理模型存在固有误差与不确定性，此类认知不确定性可能导致错误的支配关系判断，从而误导搜索进程。现有方法通过整合高斯过程回归得到的不确定性估计来修正支配判断，然而这些方法受限于GPR，其优化策略无法推广至其他不确定性量化方法。此外，基于GPR的代理模型存在计算成本高昂的问题。我们提出一种简单而有效的双重排序策略，该策略能灵活利用不同代理模型的预测结果与不确定性估计。通过同时采用基于代理模型的适应度值和基于不确定性感知的适应度值对候选解进行非支配排序，所提方法优先选择兼具高质量与高可靠性的候选解。通过包含消融实验、灵敏度实验及对比实验在内的广泛实验评估，我们验证了所提双重排序策略在不同代理模型下的有效性与鲁棒性。该双重排序框架为数据受限的实际应用场景提供了更稳健的解决方案。