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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

  • Xia, Bin ;
  • Ren, Ziyan ;
  • Zhang, Yanli ;
  • Koh, Chang-Seop
  • Received : 2012.08.02
  • Accepted : 2014.02.14
  • Published : 2014.09.01

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, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.

Keywords

Ordinary Kriging;Spherical covariance model;Surrogate model;Switched reluctance motor;Torque ripple

References

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Cited by

  1. A New Surrogate-assisted Robust Multi-objective Optimization Algorithm for an Electrical Machine Design pp.2093-7423, 2019, https://doi.org/10.1007/s42835-019-00120-1
  2. A Novel 3-D Analytical Modeling Method of Trapezoidal Shape Permanent Magnet Halbach Array for Multi-objective Optimization vol.14, pp.2, 2019, https://doi.org/10.1007/s42835-019-00109-w

Acknowledgement

Supported by : NRF of Korea