Design for six sigma has become increasingly important in complex optimization work considering uncertainty. In this paper, we present a six sigma robust optimization method based on a pseudo single-loop optimization strategy and an ensemble of random forest regression and deep belief networks (RFR-DBN). To verify its validity, we take a lightweight passenger car seat with insufficient samples as an example. We utilize intractable insufficient samples in a complex optimization problem to learn the key features for various responses and extract them separately for surrogate models from the RFR-DBN. In addition, by employing multi-island genetic algorithm and Monte Carlo simulation based on descriptive sampling, we perform quality improvement and quality assessment to find the optimal solution. Through the pseudo single-loop optimization strategy, we avoid extensive calculations in the optimization process. We demonstrate from the analytical results that the proposed method is a solution to the efficiency of optimization and insufficient samples.

在考虑到不确定性的复杂优化工作中，6 西格玛的设计变得越来越重要。在本文中，我们提出了一种基于伪单循环优化策略和随机森林回归和深度置信网络 (RFR-DBN) 集成的六西格玛鲁棒优化方法。为了验证其有效性，我们以样本不足的轻型客车座椅为例。我们在复杂的优化问题中利用难以处理的不足样本来学习各种响应的关键特征，并从 RFR-DBN 中为代理模型分别提取它们。此外，通过采用基于描述性采样的多岛遗传算法和蒙特卡罗模拟，我们进行质量改进和质量评估，以找到最优解。通过伪单循环优化策略，我们避免在优化过程中进行大量计算。我们从分析结果中证明，所提出的方法是优化效率和样本不足的解决方案。