为了解决麻雀搜索算法存在迭代后期搜索多样性不足、容易陷入局部最优等问题，提出了一种基于莱维飞行扰动策略的改进麻雀搜索算法。首先借鉴Sin混沌搜索机制，改进种群初始化策略。然后在麻雀种群觅食搜索过程中引入莱维飞行扰动机制，牵引种群移动适当的步长，增加空间搜索的多样性。最后对14个典型高维测试函数进行实验的结果表明：所提出的算法相比于传统的麻雀搜索算法和新提出的混沌麻雀搜索算法与改进麻雀搜索算法，在保持算法全局寻优能力的基础上大幅度提高了收敛速度和求解精度，能有效避免搜索过程陷入局部最优的情况，寻优率高，收敛能力强，可用于解决多峰及高维空间优化问题。
In order to solve the problems of insufficient search diversity in late iteration and easy falling of local optimization in traditional sparrow search algorithm, an improved sparrow search algorithm (ISSA) based on Levy flight disturbance strategy is proposed. Firstly, the algorithm uses Sin chaos search mechanism to improve population initialization strategy. Secondly, in the process of sparrow population foraging search, Levy flight disturbance mechanism is introduced to drag the appropriate step of population movement, and the diversity of spatial search is then increased. Finally, experiment on 14 typical highdimensional test functions has been carried out, and the results show that compared with the traditional sparrow search algorithm and two other recently proposed chaos sparrow search algorithm (CSSA) and ISSA, the proposed algorithm in this paper can effectively avoid the search process falling into local optimization, and achieve high optimization rate and strong convergence ability, and shows feasibility in solving problems of multi-peak and high-dimensional space optimization.
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