融合多策略的改进麻雀搜索算法.

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. [ABSTRACT FROM AUTHOR]