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Shape Parameter Optimization in Radial Basis Function by Grey Wolf Method
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DOI：10.1142/s0219876226500283.png)
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Abstract
En 中文
This paper presents research on optimizing the shape parameter in radial basis function (RBF) interpolation through the implementation of the grey wolf optimization (GWO) algorithm to enhance efficiency and effectiveness in two-dimensional function interpolation and image zooming task. The methodology involves testing the method on benchmark functions and image datasets, then comparing the outcomes with MATLAB's GlobalSearch algorithm and the selection of random parameters. Although the RBF consistently demonstrated high-precision capability, this accuracy is predominantly achieved under severe ill-conditioning of the RBF interpolation matrix. Also, the method exhibits improved computational efficiency, performing up to 13 times faster than the GlobalSearch algorithm in image-zooming applications, highlighting the potential of nature-inspired optimization techniques in scientific computing and image processing.
Keywords:
Radial basis function (RBF)
shape parameter optimization
multiquadratics
grey wolf optimizer
image zoom-in
Journal
I
IF:
1.6
Papers:
79
Citations:
1.6K