Abstract
Though quite a number of multidisciplinary design optimization (MDO) architectures have been proposed for the optimal design of large-scale multidisciplinary systems, how their performance changes with the complexity of MDO problem varied is not well studied. In order to solve this problem, this paper presents a variable complexity problem which allows people to obtain a MDO problem with arbitrary complexity by specifying its changeable parameters, such as the number of disciplines and the numbers of design variables. Then four investigations are performed to evaluate how the performance of different MDO architectures changes with the number of disciplines, global variables, local variables, and coupling variables varied, respectively. Finally, the results supply guidance for the selection of MDO architectures in solving practical engineering problems with different complexity.
Highlights
Multidisciplinary design optimization (MDO) is a growing field of research in the design of large-scale engineering systems that consist of a number of interacting subsystems
The distributed architectures usually include Concurrent Subspace Optimization (CSSO) [6, 7], Collaborative Optimization (CO) [8,9,10], Bilevel Integrated Systems Synthesis (BLISS) [11], Bilevel Integrated Systems Synthesis 2000 (BLISS-2000) [12, 13], Analytical Target Cascading (ATC) [14, 15], and MDO based on independent subspaces (MDOIS) [16]
We evaluate the performance of different MDO architectures through implementing four MDO architectures to solve the variable complexity problem under four investigations
Summary
Multidisciplinary design optimization (MDO) is a growing field of research in the design of large-scale engineering systems that consist of a number of interacting subsystems. Many of the test MDO problems are of low Mathematical Problems in Engineering dimensionality with few disciplines and variables They lack the ability to test the performance of different MDO architectures for the practical engineering problems which are often composed of hundreds of design variables, constraints, and coupling variables. The first one is to present a variable complexity problem which is a nonseparable nonlinear problem that allows people to specify its complexity, such as the number of disciplines, design variables, and coupling variables. This makes it feasible to test the performance of different MDO architectures for high complexity problem, and for problem with arbitrary complexity.
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