融合黄金正弦和曲线自适应的 多策略麻雀搜索算法.

To solve the shortcoming of the meta-heuristic sparrow search algorithm, such as early convergence, easy to fall into local optimum, poor global searchability, this paper proposed a multi-strategy sparrow search algorithm integrating golden sine and curve adaptative. Firstly, it used Chebyshev chaotic mapping to initialize the population, so that the initial solution position distribution was more homogeneous, produced high-quality initial solutions, and increased the richness of the population. Secondly, it introduced golden sine and curve adaptive weight to improve the location update method of the discoverer and the joiner, which effectively coordinated global search and local mining capabilities and accelerated the convergence speed. Finally, it selected the random walk or Cauchy-t disturbance strategy disturbed the optimal sparrow position dynamically, which improved the ability of the algorithm to jump out of the local optimum and improved the convergence accuracy. This paper selected 14 benchmark functions for testing, compared the simulation results of the proposed algorithm with other 9 metaheuristic algorithms, and used Wilcoxon rank-sum test and MAE ranking to verify the effectiveness of the proposed improvement strategy. The results prove that the proposed algorithm has a great improvement in global searchability, overcoming local optima, convergence speed, convergence accuracy, and stability [ABSTRACT FROM AUTHOR]