Robust Optimization for Multiple Response Using Stochastic Model

Due to a lot of uncertainties in the robust optimization process, especially in multiple response problems, many random factors can cast doubt on results. The aim of this paper is to propose a robust optimization method for multi-ple response considering the random factors in the robust optimization de-sign to solve the aforementioned problem. In this paper, we research the multi-response robustness optimization of the anti-rolling torsion bar using stochastic model. First, the quality loss function of anti-rolling torsion bar is determined as the optimization object, and the diameters of anti-rolling tor-sion bar are determined as the design variables. Second, the multi-response robust optimization model, considering random factors (such as the loads), is established by using the stochastic model. Finally, the Monte Carlo sampling method combined with a non-dominated sorting genetic algorithm Ⅱ (NSGA Ⅱ) is adopted to solve this robust optimization problem, and then the robust optimization solution is obtained. The research results indicate that the anti-rolling torsion bar weight decreases, and the stiffness and fatigue strength increase. Furthermore, the quality performance of the anti-rolling torsion bar gets better, and the anti-disturbance ability of the anti-rolling torsion bar gets stronger.