An adaptive intelligent collaborative optimization method based on inconsistent information

Multidisciplinary Design Optimization (MDO) is a design optimization method for dealing with large-scale and multi coupling complex engineering systems. The collaborative optimization (CO) has the characteristics of high degree of discipline autonomy, multi-level optimization and distributed computing. It can effectively solve the design optimization problems of large-scale complex engineering systems, and has been widely used in aerospace, ship, automobile, machinery and other fields. Because of its own optimization model and principle, the CO method has the defects of low computational efficiency and difficult convergence. In this paper, in order to overcome the convergence difficulty caused by the internal definition defects of the CO method, combined with the adaptive mechanism, the position relationship between the system level optimization points and the constraint conditions is analyzed, and the adaptive penalty function is constructed based on the inconsistent information of the system. The system level optimization model of the CO method is reconstructed by transforming the system level constraints. Finally, an example is given to demonstrate the effectiveness of the proposed method.