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Aerodynamic multi-objective integrated optimization based on principal component analysis
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DOI：10.1016/j.cja.2017.05.003.png)
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摘要
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Based on improved multi-objective particle swarm optimization (MOPSO) algorithm with principal component analysis (PCA) methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign) platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem. (C) 2017 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.
Keyword:
Aerodynamic optimization
Dimensional reduction
Improved multi-objective particle swarm optimization (MOPSO) algorithm
Multi-objective
Principal component analysis
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