摘要
针对麻雀搜索算法收敛速度慢、难以跳出局部最优等问题,提出一种基于t分布变异的改进麻雀搜索算法。在更新麻雀种群加入者位置后,引入自适应t分布变异,对加入者位置产生扰动,避免陷入局部最优,增强算法性能;通过比较灰狼优化算法、飞蛾火焰优化算法和原始麻雀搜索算法,在6个基准函数上进行仿真实验。实验结果和Wilcoxon符号秩检验结果都表明所提出的改进麻雀搜索算法的收敛精度与速度均优于其他算法,达到提高算法收敛速度,增强算法跳出局部极值能力的效果。
Aiming at the problems of slow convergence speed and difficult to jump out of local optimization of sparrow search algorithm,an improved sparrow search algorithm based on t-distribution mutation is proposed.After updating the position of the follower,the adaptive t-distribution mutation is introduced to disturb the position of the follower,avoid falling into local optimization and enhance the performance of the algorithm.By comparing gray wolf optimizer,moth-flame optimization and original sparrow search algorithm,simulation experiments are carried out on six benchmark functions.The experimental results and Wilcoxon′s signed rank test results both show that the convergence accuracy and speed of the proposed improved sparrow search algorithm are better than other algorithms,which can improve the convergence speed of the algorithm and enhance the ability of the algorithm to jump out of local extremum.
作者
吴超略
韦文山
郭羿
邬贵昌
Wu Chaolue;Wei Wenshan;Guo Yi;Wu Guichang(College of Electronic Information,Guangxi University for Nationalities,Nanning 530006,China)
出处
《网络安全与数据治理》
2022年第8期74-78,共5页
CYBER SECURITY AND DATA GOVERNANCE
基金
广西民族大学研究生教育创新计划项目(gxun-chxps202083)。
关键词
麻雀搜索算法
智能优化算法
Sin映射
T分布
sparrow search algorithm
intelligent optimization algorithm
Sin map
t-distribution
