Identifying regions of interest (RoI) in the design space is extremely useful while building metamodels with limited computational budget. Self-organizing maps (SOM) are used as a visualization technique for design space exploration that permits identifying RoI. Conventional implementation of SOM is susceptible to folds or intersections that hinder visualizing the design space. This work proposes a modified SOM algorithm whose maps are interpretable and that does not fold and allows smoother input and performance space visualization. The modified algorithm enables identification of RoI and additional sampling in the identified RoI allows building accurate Kriging metamodel, which is then used for optimization. The proposed approach is demonstrated on benchmark nonlinear analytical examples and two practical engineering design examples. Results show that the proposed approach is highly efficient in identifying the RoI and in obtaining the optima with less samples.

在建立计算预算有限的元模型时，识别设计空间中的感兴趣区域（RoI）非常有用。自组织图（SOM）用作可视化技术，用于设计空间探索，可以识别RoI。SOM的常规实现方式容易受到折叠或交叉的影响，从而阻碍了可视化设计空间。这项工作提出了一种改进的SOM算法，该算法的地图是可解释的，不会折叠，并且可以使输入和性能空间的显示更加平滑。修改后的算法可以识别RoI，并且在已识别的RoI中进行其他采样可以构建准确的Kriging元模型，然后将其用于优化。在基准非线性分析示例和两个实际工程设计示例中论证了该方法。