基于切线飞行的麻雀搜索算法

为解决在临近全局最优条件下，原始麻雀搜索算法(sparrow search algorithm, SSA)存在种群多样性降低，局部开发能力薄弱导致不容易跳出局部最优点的问题，提出基于切线飞行的麻雀搜索算法(tangent flight sparrow search algorithm, tanSSA)。首先，使用自适应t分布策略改进发现者位置更新公式，可以提高麻雀个体的寻优能力，同时防止算法早熟。然后，利用切线搜索算法中切线飞行策略所具有的可以增强算法探索搜索空间能力，且能使算法跳出局部最优解的优势，在原始麻雀搜索算法中使用切线飞行扰动策略对最优解进行扰动。这两种策略相结合，可以有效提升tanSSA算法的勘探与开发性能。最后，使用12个标准基准测试函数，结合Wilcoxon秩和检验来测试验证tanSSA算法的优化性能，并与原始SSA算法、鲸鱼优化算法、粒子群优化算法以及自适应t分布SSA算法进行比较。实验证明，基于切线飞行的麻雀搜索算法的寻优能力和收敛速度都有显著提升。

Sparrow search algorithm based on tangent flight

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.