Abstract: An improved elite sparrow search algorithm based on double-sample learning and single-dimension search is proposed to solve the problems of uneven initial population distribution,little information exchange between populations,easy to fall into local optimum and slow convergence.First,the combination of Hammersley low difference sequence and reverse learning is used to generate the initial elite population to enhance individual quality and diversity.Then,the two-sample learning strategy is adop-ted to improve the follower's position updating formula,strengthen the information exchange between populations,and improve the algorithm's ability to jump out of local optimum.Finally,in the late iteration of the algorithm,the single-dimensional search mode is adopted to enhance the depth mining ability of the algorithm and improve the accuracy of the algorithm.By analyzing the time complexity,it is proved that the improved algorithm does not increase the time complexity of the algorithm.Twelve test functions with different characteristics are selected for optimization,and the test results show that the algorithm has obvious advantages in convergence speed,accuracy and stability compared with other algorithms.

Key words: Sparrow search algorithm, Hammersley low difference sequence, Reverse learning, Two-sample learning, Single-dimension search