【摘要】 为了求解大规模多学科优化设计问题，目前已有多种分布式多学科优化方法被提出，但这些优化方法数值收敛困难，或局限于拟可分多学科设计优化问题，不适用于具有全局约束和全局目标的一般性多学科优化问题。为此本文提出一种分布式分解优化方法，其主要思想是通过两层分解策略，将一般性多学科优化问题分解为一个系统级问题和多个并行子优化问题，并证明该方法的最优解收敛于一般性多学科优化问题的Karush-Kuhn-Tucker点。最后，通过两个典型多学科优化算例对所提方法进行测试验证。结果表明该方法是一种具有良好收敛性能、竞争力强的分布式多学科优化方法。更多还原

【Abstract】 Many distributed multidisciplinary design optimization(MDO) methods have been presented for the optimal design of large-scale multidisciplinary systems, some of them are known to have numerical convergence difficulties while some of them are restricted to the quasi-separable MDO problem. For solving the general MDO problem with global constraint and objective, a distributed decomposition method is presented to decompose the general MDO problem into one system-level problem and multiple concurrent sub optimization problems through a two-level decomposition process, whose solution is proven to converge to the Karush-Kuhn-Tucker point of the general MDO problem. Two typical cases are introduced to investigate the performance of the presented method.The results show that the presented method is a competitive distributed MDO method with a good convergence performance.更多还原