Efficient multi-fidelity simulation optimization
Efficient multi-fidelity simulation optimization
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DOI：
10.1109/wsc.2014.7020219
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Jie Xu;Si Zhang;Edward Huang;Chun-Hung Chen;L. Lee;N. Çelik
中科院分区：
文献类型：
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作者：
Jie Xu;Si Zhang;Edward Huang;Chun-Hung Chen;L. Lee;N. Çelik
对于一个复杂的系统，通常有不同保真度的仿真模型。高保真模拟准确但耗时。因此，它们只能应用于少数解决方案。低保真仿真速度更快，可以评估大量的解决方案。但他们的结果可能包含显著的偏差和可变性。我们提出了一个多保真度优化与顺序变换和最佳采样（MO2TOS）框架，利用高保真和低保真模拟的好处，有效地确定一个（近）最佳的解决方案。MO2TOS对所有解决方案使用低保真仿真，然后根据低保真仿真结果为解决方案分配固定预算的高保真仿真。我们通过理论分析和数值实验与确定性模拟和随机模拟的噪声是可以忽略不计的充分复制MO2TOS的好处。我们将MO2TOS与平均分配（EA）和最优计算预算分配（OCBA）进行比较。MO2TOS始终优于EA和OCBA。
Simulation models of different fidelity levels are often available for a complex system. High-fidelity simulations are accurate but time-consuming. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are faster and can evaluate a large number of solutions. But their results may contain significant bias and variability. We propose an Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) framework to exploit the benefits of high- and low-fidelity simulations to efficiently identify a (near) optimal solution. MO2TOS uses low-fidelity simulations for all solutions and then assigns a fixed budget of high-fidelity simulations to solutions based on low-fidelity simulation results. We show the benefits of MO2TOS via theoretical analysis and numerical experiments with deterministic simulations and stochastic simulations where noise is negligible with sufficient replications. We compare MO2TOS to Equal Allocation (EA) and Optimal Computing Budget Allocation (OCBA). MO2TOS consistently outperforms both EA and OCBA.