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JACIII Vol.22 No.2 pp. 271-279
doi: 10.20965/jaciii.2018.p0271
(2018)

Paper:

A New Hybrid Method for Parameter Optimization of SVR

Jiang Xie*, Taifeng Sun*, Jieyu Zhang**, and Wu Zhang*

*School of Computer Engineering and Science, Shanghai University
No.99, Shangda Road, Shanghai 200444, China

**School of Material Science and Engineering, Shanghai University
No.99, Shangda Road, Shanghai 200444, China

Received:
July 22, 2017
Accepted:
January 22, 2018
Published:
March 20, 2018
Keywords:
support vector regression, particle swarm optimization, scatter search, parameter optimization, grain size prediction
Abstract

The performance of Support Vector Regression (SVR) depends heavily on its parameters, but some optimization methods based on Grid Search (GS) or evolutionary algorithms still have several issues that must be addressed. This paper proposes a new hybrid method (PSO-SS) that combines Particle Swarm Optimization (PSO) and Scatter Search (SS) to optimize the parameters of the SVR. In PSO-SS, to improve the search capability of PSO and reduce the likelihood of the PSO becoming trapped in the local optimum, the initial PSO population is generated by the diversification generation method and the improvement method of SS, and the velocity updating formula of PSO is improved by adding diversity information. On the StatLib and UCI datasets, our experiments show that the PSO-SS method is an effective parameter optimization method compared with other methods. In addition, an SVR model with its parameters optimized by PSO-SS (PSO-SS-SVR) is used to predict the grain size of aluminum alloys. The experimental results show that the PSO-SS-SVR method outperforms Back Propagation Neural Network (BPNN), PSO-SVR and the empirical model.

Cite this article as:
J. Xie, T. Sun, J. Zhang, and W. Zhang, “A New Hybrid Method for Parameter Optimization of SVR,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.2, pp. 271-279, 2018.
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Last updated on Aug. 16, 2018