English(EN) Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation

新的MOBO方法解耦收敛与发散，以改进优化效果

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员提出了一种名为“先收敛后发散”（CTD）的多目标贝叶斯优化（MOBO）新方法。该方法将优化过程解耦为两个不同的阶段：首先关注收敛到帕累托前沿上的单一点，然后转向发散以将解分布到整个前沿。CTD在评估预算有限或高维问题的情况下尤其有效，在绝大多数测试案例中表现优于最先进的方法。 AI

影响这种新颖的优化方法有望实现更高效的AI模型训练和超参数调优，尤其是在资源受限的环境中。

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arXiv cs.AI TIER_1 English(EN) · Chao Jiang, Yueling Huang, Miqing Li · 2026-09-15 04:00

收敛再发散：多目标贝叶斯优化中的收敛与发散解耦

arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a…