In order to improve the performance of a surrogate model-based optimization method for building optimization problems, a new active sampling strategy employing a committee of surrogate models is developed. This strategy selects new samples that are in the regions of the parameter space where the surrogate model predictions are highly uncertain and have low energy use. Results show that the new sampling strategy improves the performance of surrogate model-based optimization method. A comparison between the surrogate model-based optimization methods and two simulation-based optimization methods shows better performance of surrogate model-based optimization methods than a simulation-based optimization method using the PSO algorithm. However, the simulation-based optimization using Ant Colony Optimization found better results in terms of optimality in later stages of the optimization. However, the proposed method showed a better performance at the early optimization stages, yielding solutions within 1% of the best solution found in the fewest number of simulations.

为了提高用于构建优化问题的基于替代模型的优化方法的性能，开发了一种采用替代模型委员会的新的主动采样策略。此策略选择参数空间中替代模型预测高度不确定且能耗低的区域中的新样本。结果表明，新的采样策略提高了基于代理模型的优化方法的性能。基于代理模型的优化方法与两种基于仿真的优化方法之间的比较显示，与使用PSO算法的基于仿真的优化方法相比，基于代理模型的优化方法具有更好的性能。然而，基于蚁群优化的基于仿真的优化在优化的后期阶段发现了更好的结果。但是，所提出的方法在早期优化阶段显示出更好的性能，所产生的解决方案与在最少数量的仿真中找到的最佳解决方案的误差在1％以内。