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Linear discriminant analysis (LDA) is one of the most important supervised linear dimensional reduction techniques which seeks to learn low-dimensional representation from the original high-dimensional feature space through a transformation matrix, while preserving the discriminative information via maximizing the between-class scatter matrix and minimizing the within class scatter matrix. However,...
This paper concentrates on the application of blade-end treatment to axial compressors by means of the optimization algorithm. The blade-end treatment reduces the end wall losses and extends the stable margin by modifying blade shape near the end wall region. It contains three types of passive flow control measures, i.e., the end-bend, end-dihedral and end-sweep treatment. Firstly, the effects of...
In this paper, A self-adaptive strategy to determine the control parameters of Differential Evolution (DE) is proposed based on the elaborate analysis of intrinsic structure. The projection information of fitness function in differential direction is used to get the scale factor, while the difference between the local distance and global search range is applied to determine the crossover rate. This...
In this paper, an improved Differential Evolution (DE) with a self-adaptive strategy to determine the control parameters is proposed to solve constrained real-parameter optimization, combined with the dynamic constraint-handling mechanism. It is implemented by restating the single-objective constrained optimization as a set of single-objective unconstrained problems and dynamically assigning to the...
This paper presents E-convex linear programming problem from the theorems and expands the single E-convex programming into double. And also E-convex programming definition and some theorems are given, which enriched the field of bilevel linear programming.
In this paper, Differential Evolution based approach with a novel dynamic constraint-handling mechanism is proposed to solve constrained real-parameter optimization. This is implemented by restating the single-objective constrained optimization as a set of single-objective unconstrained problems and dynamically assigning to the individual adaptively as its fitness. Three selection criteria based on...
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