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Impacts of problem scale and sampling strategy on surrogate model accuracy: An application of surrogate-based optimization in building design

机译:问题规模和抽样策略对替代模型准确性的影响:基于替代的优化在建筑设计中的应用

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Surrogate-based Optimization is a useful approach when the objective function is computationally expensive to evaluate, compared to Simulation-based Optimization. In the surrogate-based method, analytically tractable “surrogate models” (also known as “Response Surface Models - RSMs” or “metamodels”), are constructed and validated for each optimization objective and constraint at relatively low computational cost. They are useful for replacing the time-consuming simulations during the optimization; quickly locating the area where the optimum is expected to be for further search; and gaining insight into the global behavior of the system. Nevertheless, there are still concerns about the surrogate model accuracy and the number of simulations necessary to get a reasonably accurate surrogate model. This paper aims to unveil: 1) the possible impacts of problem scale and sampling strategy on the surrogate model accuracy; and 2) the potential of Surrogate based Optimization in finding high quality solutions for building envelope design optimization problems. For this purpose, a series of multi-objective optimization test cases that mainly consider daylight and energy performance were conducted within the same time frame. Then, the results were compared, in pair, based on which discussions were made. Finally, the corresponding conclusions were obtained after the comparative study.
机译:与基于模拟的优化相比,当目标函数的计算成本很高时,基于代理的优化是一种有用的方法。在基于代理的方法中,以相对较低的计算成本为每个优化目标和约束构建并验证了易于分析处理的“代理模型”(也称为“响应表面模型-RSM”或“元模型”)。它们对于在优化过程中替换耗时的仿真很有用;快速找到最佳位置,以便进一步搜索;并深入了解系统的整体行为。然而,仍然存在关于替代模型准确性和获得合理精确替代模型所必需的模拟数量的担忧。本文旨在揭示:1)问题规模和抽样策略对替代模型准确性的可能影响; 2)基于代理优化的潜力,可以为建筑围护结构优化问题寻找高质量的解决方案。为此,在同一时间范围内进行了一系列主要考虑日光和能源性能的多目标优化测试案例。然后,根据讨论结果成对比较结果。最后,通过比较研究得出了相应的结论。

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