CONTINUOUS SIMULATION OPTIMIZATION WITH MODEL MISMATCH USING GAUSSIAN PROCESS REGRESSION
CONTINUOUS SIMULATION OPTIMIZATION WITH MODEL MISMATCH USING GAUSSIAN PROCESS REGRESSION
复制标题
使用高斯过程回归进行模型失配的连续仿真优化
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
S. Pokutta
中科院分区：
文献类型：
--
作者：
Alireza Inanlouganji;Giulia Pedrielli;Georgios Fainekos;S. Pokutta
多保真度模拟优化是一个新兴领域，关注利用低保真度（计算成本低但不准确）模型来优化高保真度（昂贵且准确）模型。在此背景下，低保真度模型与高保真度模型存在不匹配，高保真度模型的值可通过查询昂贵的模拟器逐点获得。在此，提出了一种用于连续全局优化的高效多保真度算法。该算法由一个加法模型组成，该模型整合了低保真度和偏差（不匹配）预测。引入了两种不同使用累积的高保真度和低保真度信息的采样准则，以及一个低成本的判定标准，用于指导是否从昂贵的模拟器中采样的决策。使用一种最先进的随机搜索基准算法对所提出算法的性能进行了评估。结果表明，所提出的方法能够以更高的精度超越基准，同时在昂贵模拟次数方面基本保持相同的性能。
Multi-fidelity simulation optimization is an emerging area looking at the use of low-fidelity (computationally cheap but inaccurate) models to optimize high-fidelity (expensive and accurate) models. In this context, low-fidelity models exhibit a mismatch to high-fidelity models whose values can be point-wise obtained by querying an expensive simulator. Herein, an efficient multi-fidelity algorithm is proposed for continuous global optimization. The algorithm is made up of an additive model that consolidates low-fidelity and bias (mismatch) predictions. Two sampling criteria with different use of the cumulated high and low-fidelity information are introduced as well as a cheap certificate guiding the decision on whether to sample from the expensive simulator. The performance of proposed algorithms is evaluated using a state of the art stochastic search benchmark algorithm. The results show that the proposed methods can beat the benchmark with improved accuracy, while essentially maintaining the same performance in terms of number of expensive simulations.