Nonlinear variable selection algorithms for surrogate modeling
Nonlinear variable selection algorithms for surrogate modeling
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DOI：
10.1002/aic.16601
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发表时间：
2019-08
期刊：
影响因子：
3.7
通讯作者：
Jianyuan Zhai;Fani Boukouvala
中科院分区：
文献类型：
--
作者：
Jianyuan Zhai;Fani Boukouvala
分析、模拟和优化复杂系统的能力在所有工程学科中变得越来越重要。使用复杂系统进行决策通常会导致非线性优化问题，这些问题依赖于计算成本高昂的模拟。因此，检测优化问题的实际结构并用封闭形式的解析表达式来表达这些问题通常是具有挑战性的。基于代理的复杂系统优化是一种很有前途的方法，它基于自适应拟合和优化输入输出数据近似值的概念。标准的基于替代的优化假设自由度是先验已知的;然而，在真实的应用中，黑盒公式的稀疏性和实际结构可能是未知的。在这项工作中，我们建议选择正确的变量有助于每个目标函数和黑盒问题的约束，通过制定作为一个非线性特征选择问题的制定的真实稀疏性的识别。我们比较了基于支持向量回归的三个变量选择标准，并开发了有效的算法来检测黑盒公式的稀疏性，当只有有限数量的确定性或噪声数据可用时。
Having the ability to analyze, simulate, and optimize complex systems is becoming more important in all engineering disciplines. Decision‐making using complex systems usually leads to nonlinear optimization problems, which rely on computationally expensive simulations. Therefore, it is often challenging to detect the actual structure of the optimization problem and formulate these problems with closed‐form analytical expressions. Surrogate‐based optimization of complex systems is a promising approach that is based on the concept of adaptively fitting and optimizing approximations of the input–output data. Standard surrogate‐based optimization assumes the degrees of freedom are known a priori; however, in real applications the sparsity and the actual structure of the black‐box formulation may not be known. In this work, we propose to select the correct variables contributing to each objective function and constraints of the black‐box problem, by formulating the identification of the true sparsity of the formulation as a nonlinear feature selection problem. We compare three variable selection criteria based on Support Vector Regression and develop efficient algorithms to detect the sparsity of black‐box formulations when only a limited amount of deterministic or noisy data is available.