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Uncertainties, including aleatory and epistemic uncertainties, always exist in multidisciplinary system. Due to the discontinuous nature of epistemic uncertainty and the complex coupled relation among subsystems, the computational efficiency of reliability-based multidisciplinary design optimization (RBMDO) with mixed aleatory and epistemic uncertainties is extremely low. A novel RBMDO procedure is presented in this paper based on combined probability theory and evidence theory (ET) to deal with hybrid-uncertainties and improve the computational efficiency. Firstly, based on Bayes method, a novel method to define the probability density function of the aleatory variables is proposed. Secondly, the conventional equivalent normal method (J-C method) is modified to reliability analysis with hybrid-uncertainties. Finally, a novel RBMDO procedure is suggested by integrating the modified J-C method into the frame of sequence optimization and reliability analysis (SORA). Numerical examples and engineering example are applied to demonstrate the performance of the proposed method. The examples show the excellence of the RBMDO method both in computational efficiency and accuracy. The proposed method provides a practical and effective reliability design method for multidisciplinary system.

Collaborative optimization (CO) method is widely used in solving multidisciplinary design optimization (MDO) problems, yet its computation requirement has been an obstacle to the applications, leading to doubts about CO's convergence property. The feasible domain of CO problem is first examined and it is proven that feasible domain remains the same during the CO formulation. So is the same with extreme points. Then based on contemporary research conclusion that the system-level optimization problem suffers from inherent computational difficulties, it is further pointed out that the employment of meta-heuristic optimization methods in CO could eliminate these difficulties. To make CO more computational feasible, a new method collaborative optimization with dimension reduction (CODR) is proposed. It focused on optimization dimension reduction and lets local copy of common shared design variables equal system shared design variables directly. Thus, the number of dimensions that CODR could reduce equal the number of common shared design variables. Numerical experiment suggests that CODR reduces computations greatly without losing of optimization accuracy.