文章基本信息

标题：The chaos differential evolution optimization algorithm and its application to support vector regression machine
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作者：Liang, Wei ; Zhang, Laibin ; Wang, Mingda 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2011
卷号：6
期号：7
页码：1297-1304
DOI：10.4304/jsw.6.7.1297-1304
语种：English
出版社：Academy Publisher
摘要：The Differential Evolution (DE) population-based algorithm is an optimal algorithm with powerful global searching capability, but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaos Differential Evolution algorithm (CDE) based on the cat map is proposed which combines DE and chaotic searching algorithm. Firstly, the chaotic distributed superiority of the cat map is analyzed in this paper. Secondly, the detailed implementation of CDE is introduced. Finally, the effectiveness of CDE is verified in the numerical tests. The Support Vector Regression machine (SVR) is an effective tool to solve the problem of nonlinear prediction, but its prediction accuracy and generalization performances depend on the selection of parameters greatly. So, the CDE is applied to SVR to build an optimized prediction model called CDE-SVR. Then the new prediction model is applied to the short-time regression prediction of the chaotic time series and the boundary extension of the mechanical vibration signals. The results of the two experiments demonstrate the effectiveness of the CDE-SVR.
关键词：differential evolution;chaotic cat map;support vector regression machine;parameters optimization;boundary extension