In surrogate-based optimization (SBO), the deception issues associated with the low fidelity of the surrogate model can be dealt with in situ model refinement that uses infill points during optimization. However, there is a lack of model refinement methods that are both independent of the choice of surrogate model (neural networks, radial basis functions, Kriging, etc.) and provides a methodical approach to preserve the fidelity of the search dynamics, especially in the case of population-based heuristic optimization processes. This paper presents an adaptive model refinement (AMR) approach to fill this important gap. Therein, the question of when to refine the surrogate model is answered by a novel hypothesis testing concept that compares the distribution of model error and distribution of function improvement over iterations. These distributions are respectively computed via a probabilistic cross-validation approach and by leveraging the probabilistic improvement information uniquely afforded by population-based algorithms such as particle swarm optimization. Moreover, the AMR method identifies the size of the batch of infill points needed for refinement. Numerical experiments performed on multiple benchmark functions and an optimal (building energy) planning problem demonstrate AMR’s ability to preserve computational efficiency of the SBO process while providing solutions of more attractive fidelity than those provisioned by a standard SBO approach.

在基于代理的优化（SBO）中，与代理模型的低保真度相关的欺骗问题可以通过在优化过程中使用填充点的原位模型优化来解决。但是，缺乏既不依赖于替代模型的选择（神经网络，径向基函数，Kriging等）又无法提供一种方法方法来保持搜索动态保真度的方法，特别是在模型优化中。基于人口的启发式优化过程的案例。本文提出了一种自适应模型优化（AMR）方法来填补这一重要空白。其中，何时优化代理模型的问题由新颖的假设检验概念回答，该概念比较了模型误差的分布和迭代中函数改进的分布。这些分布分别通过概率交叉验证方法并利用诸如粒子群优化等基于种群的算法唯一提供的概率改进信息来计算。此外，AMR方法可确定精炼所需的一批填充点的大小。在多个基准功能和最佳（建筑能耗）规划问题上进行的数值实验表明，AMR能够保持SBO过程的计算效率，同时提供比标准SBO方法所提供的解决方案更具吸引力的保真度的解决方案。此外，AMR方法可确定精炼所需的一批填充点的大小。在多个基准功能和最佳（建筑能耗）规划问题上进行的数值实验表明，AMR能够保持SBO过程的计算效率，同时提供比标准SBO方法所提供的解决方案更具吸引力的保真度的解决方案。此外，AMR方法可确定精炼所需的一批填充点的大小。在多个基准功能和最佳（建筑能耗）规划问题上进行的数值实验表明，AMR能够保持SBO过程的计算效率，同时提供比标准SBO方法所提供的解决方案更具吸引力的保真度的解决方案。