Aiming at the shortcomings of sine cosine algorithm(SCA),such as low optimization accuracy,slow convergence speed,and easy to fall into local optimal value,a hybrid sine-cosine algorithm combining Cauchy distribution and Gaussian disturbance was proposed.Cubic chaotic mapping was used to initialize the population of the algorithm to improve the individual quality of the population.An adaptive energy factor was constructed to balance the ability of global exploration and local exploitation of the algorithm.The inverse cumulative distribution function method of Cauchy distribution was used to update individual position and strengthen the global exploration capability of the algorithm.The hybrid algorithm executes SCA algorithm and Gaussian perturbation strategy conditionally according to the strategy selection factor,increases the population diversity in the late iteration,reduces the probability of the algorithm falling into the local extreme value,and improves the optimization accuracy and convergence speed of the algorithm.Through the verification of 10 classical test functions in low and high dimensions,the proposed algorithm has higher optimization accuracy and faster convergence speed than the comparison algorithm.
关键词
正余弦优化算法/Cubic映射/柯西分布/逆累积分布函数/高斯扰动
Key words
sine cosine algorithm/Cubic map/Cauchy Distribution/inverse cumulative distribution function/Gauss disturbance
维普
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