Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,...Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective 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.展开更多
Spectral regression discriminant analysis(SRDA)is one of the most popular methods for large-scale discriminant analysis.It is a stepwise algorithm composed of two steps.First,the response vectors are obtained from sol...Spectral regression discriminant analysis(SRDA)is one of the most popular methods for large-scale discriminant analysis.It is a stepwise algorithm composed of two steps.First,the response vectors are obtained from solving an eigenvalue problem.Second,the projection vectors are computed by solving a least-squares problem.However,the independent two steps can not guarantee the optimality of the two terms.In this paper,we propose a unified framework to compute both the response matrix and the projection matrix in SRDA,so that one can extract the discriminant information of classification tasks more effectively.The convergence of the proposed method is discussed.Moreover,we shed light on how to choose the joint parameter adaptively,and propose a parameter-free joint spectral regression discriminant analysis(JointSRDA-PF)method.Numerical experiments are made on some real-world databases,which show the numerical behavior of the proposed methods and the effectiveness of our strategies.展开更多
基金supported by the National Natural Science Foundation of China (No.11402288)
文摘Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective 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.
基金the anonymous referees and the editor for insightful comments and suggestions that greatly improved the representation of this paper.
文摘Spectral regression discriminant analysis(SRDA)is one of the most popular methods for large-scale discriminant analysis.It is a stepwise algorithm composed of two steps.First,the response vectors are obtained from solving an eigenvalue problem.Second,the projection vectors are computed by solving a least-squares problem.However,the independent two steps can not guarantee the optimality of the two terms.In this paper,we propose a unified framework to compute both the response matrix and the projection matrix in SRDA,so that one can extract the discriminant information of classification tasks more effectively.The convergence of the proposed method is discussed.Moreover,we shed light on how to choose the joint parameter adaptively,and propose a parameter-free joint spectral regression discriminant analysis(JointSRDA-PF)method.Numerical experiments are made on some real-world databases,which show the numerical behavior of the proposed methods and the effectiveness of our strategies.
