融合多策略的改进麻雀搜索算法

针对麻雀搜索算法收敛速度缓慢、寻优精度不足和容易陷入局部最优等缺点，提出了一种融合正弦搜索策略和多样性变异处理策略的改进麻雀搜索算法。通过引入正弦搜索策略，自适应调整个体权重提高算法收敛速度；针对个体聚集程度过高问题，采用多样性变异处理，引入生物学中种群聚集度的概念和柯西变异对最优解进行扰动，提高算法逃离局部最优的可能。通过九个不同特征的基准函数进行寻优测试，测试结果表明改进算法能够更快地收敛于最优值，有更好的平均值和标准差，表明了其具备更优的收敛速度、收敛稳定性和逃离局部最优值的能力。通过应用该改进优化算法于分数阶PID控制器的参数整定上，进一步验证了改进策略的有效性和可行性。

Improved sparrow search algorithm fused with multiple strategies

Aiming at the shortcomings of the sparrow search algorithm such as slow convergence speed, insufficient optimization accuracy and easy to fall into the local optimum, this paper proposed an improved sparrow search algorithm that combined the sine search strategy and the diversity mutation processing strategy. Through the introduction of a sine search strategy, adaptive adjustment of individual weights improved the convergence speed of the algorithm. Aiming at the problem of excessive individual aggregation, this paper adopted diversity mutation processing, introduced the concept of population aggregation degree in biology and Cauchy mutation to disturb the optimal solution, and improved the possibility of the algorithm escaping from the local optimal. By testing 9 benchmark functions with different characteristics, the test results show that the improved algorithm can converge to the optimal value faster, with better average and standard deviation, indicating that it has better convergence speed, convergence stability, and the ability to escape local optimal values. By applying the improved optimization algorithm to the parameter tuning of the fractional PID controller, the experimental results further verify the effectiveness and feasibility of the improved strategy.