Abstract
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.
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