Multi-discipline optimization (MDO) has been proposed to handle complex optimization problems with multiple disciplines or subsystems. These problems are with global and coupling variables that are shared by more than one subsystem, which is one of the major challenges for these problems. Diverse methods that can be classified as of single- and multi-levels have been developed aiming to solve those problems. Most of these methods do not give optimization autonomies to each subsystem concerning these shared variables. This study proposed that for decomposed bi-level MDO problems, each subsystem is given full optimization autonomy in the initial stage to decide possible best solutions individually. Then all the obtained solutions are given to the system that will process the information and dispatch shared variables to each subsystem. Based on these dispatched variables, subsystems will sequentially perform the corresponding optimization with respect to their local (and global) variables with additional consistency constraints. There is no nested optimization between the system and subsystems. Six numerical and engineering examples are tested and comparisons with other methods are made to demonstrate the efficiency and applicability of the proposed approach.
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