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