Abstract: A sparrow search algorithm based on adaptive hybrid strategy is proposed to solve the problems of low accuracy, insufficient stability and easy to fall into local optimization of sparrow search algorithm. The tent chaotic map is introduced to initialize the population, increase the population number, and merge the two populations. Then the elite population is obtained by using the elite strategy to improve the quality of the initial solution. The adaptive periodic convergence factor α is introduced to strengthen the search ability and convergence speed. The position update mode of followers and forerunners is adjusted to prevent the algorithm from falling into local optimization to a certain extent. Polynomial mutation disturbance is introduced to solve the problem of falling into local optimization of SSA. Using 12 test functions and the results show that AHSSSA has better optimization performance than SSA.

Key words: sparrow search algorithm, Tent chaotic mapping, adaptive periodic convergence factor, position update mode adjustment, polynomial variation disturbance

摘要： 针对麻雀搜索算法（sparrow search algorithm，SSA）求解精度低，稳定性不足，易陷入局部最优等问题，提出一种基于自适应混合策略的麻雀搜索算法（adaptive hybrid strategy sparrow search algorithm，AHSSSA）。引入Tent混沌映射初始化种群，增加种群数量，合并两个种群，再利用精英策略得到精英种群，以提高初始解质量；引入自适应周期收敛因子[α]，加强搜索能力与收敛速度；追随者与预警者位置更新方式调整，在一定程度上防止算法陷入局部最优；引入多项式变异扰动，以解决SSA陷入局部最优问题。利用12种测试函数进行测试，结果表明：AHSSSA比SSA有更好的寻优性能。

关键词: 麻雀搜索算法, Tent混沌映射, 自适应周期收敛因子, 位置更新方式调整, 多项式变异扰动