The chaos differential evolution optimization algorithm and its application to support vector regression machine

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.