Application of multi-objective alliance algorithm to multidisciplinary design optimization under uncertainty

Xiaoqian Chen, Xingzhi Hu, Valerio Lattarulo, Wen Yao, Yong Zhao

Abstract: Multidisciplinary design optimization (MDO) under uncertainty is increasingly being recognized in improving the performance, safety, and reliability of aerospace vehicles. However, the solution process is still challenging, especially in multi-objective optimizations. In this study, a multi-objective alliance algorithm (MOAA) is employed and corresponding computational heuristics are presented, including system decoupling strategy, active subspaces, and surrogate model. Both reliability-based design optimization (RBDO) and robust design optimization (RDO) are considered to prove the efficacy of the proposed approach, which is exemplified by the conceptual design of a small satellite mission for the Moon imaging. Among multiple system uncertainties and closely coupled disciplines, the approach exhibits high effectiveness and strong adaptability at considerably less cost, thus providing a potential approach to solving widely exiting MDO problems of aerospace vehicles.