In order to solve the problem that the original sparrow search algorithm has reduced population diversity and the weak local development ability makes it difficult to jump out of the local optimum under the condition of approaching the global optimum, this paper proposed a sparrow search algorithm based on tangential flight. First of all, it used adaptive t-distribution strategy to improve the finder location update formula, which could improve the individual sparrow's optimization ability and prevent the algorithm from maturing. Then, this paper used the tangent flight strategy in the tangent search algorithm, which could enhance the search space ability of the algorithm and make the algorithm jump out of the advantage of the local optimal solution, to disturb the optimal solution in original sparrow search algorithm. The combination of these two strategies could effectively improve the exploration and development performance of tanSSA algorithm. Finally, combining with Wilcoxon rank sum test, this paper used 12 standard benchmark functions to test and verify the optimization performance of tanSSA algorithm, and compared it with the original SSA algorithm, whale optimization algorithm, particle swarm optimization algorithm and adaptive t-distribution SSA algorithm. The experimental results show that the optimization ability and convergence speed of the sparrow search algorithm based on tangent flight have a significant improvement.

[2022-08-23] Accepted Paper /doc/2022.06.0299/v1

[2023-01-05] Printed Article /doc/2022.06.0299/v2