Computer Science ›› 2020, Vol. 47 ›› Issue (2): 206-212.doi: 10.11896/jsjkx.181102197

• Artificial Intelligence • Previous Articles     Next Articles

Sine Cosine Algorithm Based on Logistic Model and Stochastic Differential Mutation

XU Ming1,JIAO Jian-jun1,LONG Wen2   

  1. (School of Mathematics & Statistics,Guizhou University of Finance and Economics,Guiyang 550025,China)1;
    (Guizhou Key Laboratory of Economics System Simulation,Guizhou University of Finance and Economics,Guiyang 550025,China)2
  • Received:2018-11-28 Online:2020-02-15 Published:2020-03-18
  • About author:XU Ming,born in 1976,Ph.D,professor,is member of China Computer Fe-deration (CCF).His main research inte-rests include machine learning and intelligent computing;LONG Wen,born in 1977,Ph.D,professor,Ph.D supervisor.His main research interests include intelligent computing and data mining.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61463009, 11761019, 11361014), Program for the Science and Technology Top Talents of Higher Learning Institutions of Guizhou (KY070) and Guizhou Differential-Differential Dynamic System Innovation Talents Team (20175658).

Abstract: In view of the slow convergence speed,easy to fall into local optimum and low precision of the standard sine cosine algorithm,an improved sine cosine algorithm (LS-SCA) with the nonlinear conversion parameter and the stochastic differential mutation strategy was proposed to solve global optimization problems.Firstly,a nonlinear conversion parameter based on Logistic model is designed to balance between global exploration and local exploitation.Secondly,a stochastic differential mutation strate-gy is introduced to maintain the diversity of population and avoid falling into the optimal value.Finally,the nonlinear conversion parameter and stochastic differential mutation strategies are fused.On the one hand,12 standard test functions are selected for global optimization experiments.The results show that LS-SCA is superior to the other SCAs and comparison latest algorithms in convergence accuracy and convergence speed with the same number of fitness function evaluations.Stochastic differential mutation strategy can improve LS-SCA’s global optimization ability especially.On the other hand,LS-SCA is used to optimize the parameters of neural network to solve two classical classification problems.Compared with the traditional BP algorithm and the otherintelligent algorithms,the neural network based on LS-SCA can achieve higher classification accuracy.

Key words: Logistic model, Neural network, Nonlinear conversion parameter, Sine cosine algorithm, Stochastic differential mutation

CLC Number: 

  • TP301.6
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