A Sequential Robust Optimization Approach for Multidisciplinary Design Optimization With Uncertainty

One real challenge for multidisciplinary design optimization (MDO) problems to gain a robust solution is the propagation of uncertainty from one discipline to another. Most existing methods only consider an MDO problem in the deterministic manner or find a solution which is robust for a single-disciplinary optimization problem. These rare methods for solving MDO problems under uncertainty are usually computationally expensive. This paper proposes a robust sequential MDO (RS-MDO) approach based on a sequential MDO (S-MDO) framework. First, a robust solution is obtained by giving each discipline full autonomy to perform optimization without considering other disciplines. A tolerance range is specified for each coupling variable to take care of uncertainty propagation in the coupled system. Then the obtained robust extreme points of global variables and coupling variables are dispatched into subsystems to perform robust optimization (RO) sequentially. Additional constraints are added in each subsystem to keep the consistency and to guarantee a robust solution. To find a solution with such strict constraints, genetic algorithm (GA) is used as a solver in each optimization stage. The proposed RS-MDO can save significant amount of computational efforts by using the sequential optimization procedure. Since all iterations in the sequential optimization stage can be processed in parallel, this robust MDO approach can be more time-saving. Numerical and engineering examples are provided to demonstrate the availability and effectiveness of the proposed approach.