Optimal computation budget allocation with Gaussian process regression

IF 6 2区管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

European Journal of Operational Research Pub Date : 2024-12-04 DOI:10.1016/j.ejor.2024.11.049

Mingjie Hu , Jie Xu , Chun-Hung Chen , Jian-Qiang Hu

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引用次数: 0

Abstract

We consider Ranking and Selection (R&S) in the presence of spatial correlation among designs. The performance of each design can only be evaluated through stochastic simulation with heterogeneous noise. Our primary objective is to maximize the probability of correct selection (PCS) by optimally allocating the simulation budget considering the spatial correlation among designs. We propose using Gaussian process regression (GPR) to model the spatial correlation and develop a GPR-based optimal computing budget allocation (GPOCBA) framework to derive an asymptotically optimal allocation policy. Additionally, we analyze the impact of spatial correlation on allocation policy and quantify its benefits under specific cases. We also introduce a sequential implementation of GPOCBA and establish convergence results. Numerical experiments show that the proposed GPOCBA method significantly outperforms the widely used OCBA, demonstrating improved computational efficiency by considering spatial correlation in R&S problems.

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利用高斯过程回归优化计算预算分配

我们考虑排序和选择（R&；S）在设计之间的空间相关性的存在。每种设计的性能只能通过带有异质噪声的随机模拟来评估。我们的主要目标是考虑设计之间的空间相关性，通过优化分配仿真预算来最大化正确选择（PCS）的概率。本文提出利用高斯过程回归（GPR）对空间相关性进行建模，并建立基于高斯过程回归的最优计算预算分配（GPOCBA）框架来推导渐近最优分配策略。此外，我们还分析了空间相关性对分配政策的影响，并在具体案例下量化了其效益。我们还介绍了GPOCBA的顺序实现，并建立了收敛结果。数值实验表明，GPOCBA方法显著优于目前广泛使用的OCBA方法，在R&；S问题中考虑了空间相关性，提高了计算效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Operational Research 管理科学-运筹学与管理科学

CiteScore

11.90

自引率

9.40%

发文量

786

审稿时长

8.2 months

期刊介绍： The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.