论文标题
随机模拟器的贝叶斯多目标优化：帕累托主动学习方法的扩展
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method
论文作者
论文摘要
本文重点介绍了具有高输出差异的随机模拟器的多目标优化，其中输入空间是有限的，并且目标函数的评估昂贵。我们依靠贝叶斯优化算法，这些算法使用概率模型来预测要优化的功能。所提出的方法是用于估计帕累托最佳溶液的帕累托主动学习（PAL）算法的扩展，该算法使其适合随机环境。我们将其命名为随机模拟器（PALS）的Pareto主动学习。通过数值实验对一组双维，双目标测试问题进行数值实验评估了PAL的性能。与其他基于标量的和随机搜索的方法相比，PAL表现出卓越的性能。
This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate. We rely on Bayesian optimization algorithms, which use probabilistic models to make predictions about the functions to be optimized. The proposed approach is an extension of the Pareto Active Learning (PAL) algorithm for the estimation of Pareto-optimal solutions that makes it suitable for the stochastic setting. We named it Pareto Active Learning for Stochastic Simulators (PALS). The performance of PALS is assessed through numerical experiments over a set of bi-dimensional, bi-objective test problems. PALS exhibits superior performance when compared to other scalarization-based and random-search approaches.
