Classifier-guided sampling for discrete variable, discontinuous design space exploration: Convergence and computational performance
Classifier-guided sampling for discrete variable, discontinuous design space exploration: Convergence and computational performance
复制标题
用于离散变量、不连续设计空间探索的分类器引导采样：收敛和计算性能
DOI：
10.1080/0305215x.2014.908869
复制
发表时间：
2015
影响因子：
2.7
通讯作者：
C. Seepersad
中科院分区：
文献类型：
--
作者：
Peter B. Backlund;David Shahan;C. Seepersad
介绍了一种分类器引导抽样（CGS）方法，用于解决具有离散和/或连续变量和连续和/或不连续响应的工程设计优化问题。该方法融合了元模型导向抽样和基于总体的优化算法的概念。CGS方法使用贝叶斯网络分类器根据一组已知的观察值或训练点来预测新设计的性能。然而，与大多数元建模技术不同，分类器为新设计分配一个分类类标签，而不是预测连续空间中的结果响应，从而适应离散变量或分类变量的不可微和不连续函数。CGS方法使用这些分类器来指导基于总体的采样过程，使离散和/或连续变量值的组合具有高概率产生首选性能。因此，CGS方法适用于离散/不连续设计问题，这些问题不适合传统的元建模技术，而且计算成本太高，无法仅通过基于种群的算法来解决。研究了CGS方法在求解一组离散变量优化问题时的收敛速度和计算性质。结果表明，与遗传算法相比，CGS方法对已知全局最优解的平均收敛速度有显著提高。
A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodelling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates non-differentiable and discontinuous functions of discrete or categorical variables. The CGS method uses these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill suited for conventional metamodelling techniques and too computationally expensive to be solved by population-based algorithms alone. The rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, compared with genetic algorithms.