National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, China.
School of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China.
PLoS One. 2023 Oct 31;18(10):e0293318. doi: 10.1371/journal.pone.0293318. eCollection 2023.
Surrogate models are commonly used as a substitute for the computation-intensive simulations in design optimization. However, building a high-accuracy surrogate model with limited samples remains a challenging task. In this paper, a novel adaptive-weight ensemble surrogate modeling method is proposed to address this challenge. Instead of using a single error metric, the proposed method takes into account the position of the prediction sample, the mixture error metric and the learning characteristics of the component surrogate models. The effectiveness of proposed ensemble models are tested on five highly nonlinear benchmark functions and a finite element model for the analysis of the frequency response of an automotive exhaust pipe. Comparative results demonstrate the effectiveness and promising potential of proposed method in achieving higher accuracy.
代理模型通常被用作设计优化中计算密集型模拟的替代品。然而，用有限的样本构建高精度的代理模型仍然是一项具有挑战性的任务。在本文中，提出了一种新的自适应权重集成代理建模方法来解决这一挑战。与使用单一误差度量不同，所提出的方法考虑了预测样本的位置、混合误差度量以及组成代理模型的学习特性。所提出的集成模型的有效性在五个高度非线性基准函数和一个用于分析汽车排气管频率响应的有限元模型上进行了测试。比较结果表明了所提出的方法在提高精度方面的有效性和潜力。
