混合策略改进的麻雀搜索算法

针对麻雀搜索算法存在的迭代过程中种群多样性减少且容易陷入局部最优以及收敛速度慢等问题，提出混合策略改进的麻雀搜索算法（MSSSA）。利用Circle映射初始化麻雀个体位置，增加初始种群的多样性。结合蝴蝶优化算法（BOA）中蝴蝶飞行方式，改进发现者的位置更新策略，增强算法全局探索能力。采用逐维变异方法对个体位置进行扰动，提升算法跳出局部最优的能力。在仿真实验中与4种基本算法和5种改进算法基于10个基准测试函数进行比较并进行Wilcoxon秩和检验，结果表明所提算法具有更好的收敛性和求解精度，全局寻优能力得到大幅提升。

Mixed Strategy Improved Sparrow Search Algorithm

Aiming at the shortcomings of the sparrow search algorithm in the iterations of population diversity reduction, easy to fall into local optimality and slow convergence speed, a Mixed Strategy improved Sparrow Search Algorithm（MSSSA） is proposed. Circle map is used to initialize the individual positions of sparrows to increase the diversity of the initial population. Combining the butterfly optimization algorithm the location update method of the discoverer is improved to enhance global exploration ability of the algorithm. The dimensional-by-dimensional mutation method is used to perturb the individual position and improve the algorithm’s ability to jump out of the local optimum. In the simulation experiment, it compares with 4 basic algorithms and 5 improved algorithms based on 10 benchmark functions and performs Wilcoxon rank sum test. The results show that the proposed algorithm has better convergence and solution accuracy, global optimization ability has been greatly improved.