Six sigma robust optimization method based on a pseudo single-loop strategy and RFR-DBN with insufficient samples

Abstract 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.