Volume 6 Number 7 (Jul. 2011)
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JSW 2011 Vol.6(7): 1297-1304 ISSN: 1796-217X
doi: 10.4304/jsw.6.7.1297-1304

The Chaos Differential Evolution Optimization Algorithm and its Application to Support Vector Regression Machine

Wei Liang, Laibin Zhang, Mingda Wang
College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, China

Abstract—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.

Index Terms—differential evolution, chaotic cat map, support vector regression machine, parameters optimization, boundary extension


Cite: Wei Liang, Laibin Zhang, Mingda Wang, "The Chaos Differential Evolution Optimization Algorithm and its Application to Support Vector Regression Machine," Journal of Software vol. 6, no. 7, pp. 1297-1304, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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