Impacts of Problem Scale and Sampling Strategy on Surrogate Model Accuracy An Application of Surrogate-based Optimization in Building Design

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.