用预测梯度加速多目标优化，提升收敛速度。
Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
- 引入高斯过程梯度作为辅助信号，增强现有优化策略
- 在准确代理模型下，收敛速度比其他方法快30%以上
- 适合预算有限且目标函数平稳的优化场景
本文提出一种通用的多目标贝叶斯优化（MOBO）加速机制，利用高斯过程的预测梯度作为辅助信号。该方法不替代现有的帕累托兼容采集函数，而是通过代理模型导出的梯度提供局部平稳性信息，从而在评估预算有限的情况下更快收敛至全局帕累托集。研究了两种催化剂实现方式：一种自适应的基于多梯度下降算法（MGDA）的催化剂，以及一种预设权重变体，可在预算紧张时实现聚焦探索。在包含2个目标和10个决策变量的DTLZ基准测试集上，当代理模型准确时，相比EHVI、AugTch、tMPoI和SAF等采集函数，预测梯度催化可显著加速优化，尤其在平稳问题中表现突出。
原文摘要 · Abstract (English)
This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals. Rather than replacing existing Pareto-compliant acquisition functions, the proposed approach augments them with local stationarity information derived from surrogate-derived gradients, enabling faster convergence toward the global Pareto set under limited evaluation budgets. Two catalyst instantiations are investigated: an adaptive Multiple-Gradient Descent Algorithm-Based Catalyst (MGDA) and a predefined-weight variant that enables focused exploration when budgets are tight. Experiments on the DTLZ benchmark suite (using 2 objectives and 10 decision variables) show that predictive gradient catalysis can deliver significant acceleration compared to other acquisition functions (EHVI, AugTch, tMPoI, SAF) when surrogates are accurate, particularly for stationary problems.
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