NDLI: An Adaptive Optimization Algorithm Based on Kriging Interpolation with Spherical Model and its Application to Optimal Design of Switched Reluctance Motor

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An Adaptive Optimization Algorithm Based on Kriging Interpolation with Spherical Model and its Application to Optimal Design of Switched Reluctance Motor

Content Provider	Semantic Scholar
Author	Xia, Bin Ren, Ziyan Zhang, Yanli Koh, Chang Seop
Copyright Year	2014
Abstract	In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor stator, where the pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.jeet.or.kr/LTKPSWeb/uploadfiles/be/201404/010420141725006566250.pdf
Language	English
Access Restriction	Open
Subject Keyword	Adaptive optimization Algorithmic efficiency Approximation algorithm Biological Science Disciplines Computation Electromagnetic Phenomena Gallium Genetic algorithm Global optimization Interpolation Imputation Technique Iteration Kriging Lateral thinking Loss function Mathematical optimization Numerical analysis Optimal design Optimization problem Population Parameter R.O.T.O.R. Refinement (computing) Reluctance Ripple effect Sampling (signal processing) Sampling - Surgical action Sensitivity and specificity Software release life cycle Spherical model Surrogate model
Content Type	Text
Resource Type	Article

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