摘要
针对蝗虫优化算法(Grasshopper Optimization Algorithm,GOA)存在求解精度低,收敛速度慢等问题,提出具有扰动机制和强化莱维飞行的蝗虫优化算法(DLGOA),位置参数部分使用非线性曲线函数去平衡算法局部开发和全局探索;扰动因子引入位置更新公式,提高算法寻优精度、收敛速度;将莱维飞行的步长改进以避免陷入局部最优,并利用高斯分布的随机性增加种群多样性.通过对7个基准函数进行仿真实验以及使用Wilcoxon秩和检验来评价算法性能,实验结果表明DLGOA算法具有较好的鲁棒性以及寻优精度,且明显提升了收敛速度.
The grasshopper optimization algorithm(GOA)has the problems of low precision and slow convergence speed,a locust optimization algorithm(DLGOA)with perturbation mechanism and enhanced Levi flight is proposed.The position parameter part uses the non-linear curve function to balance the local development and global exploration of the algorithm;Disturbance factors are introduced into the position update formula to improve the optimization accuracy and convergence speed of the algorithm;the step length of Levi flight is improved to avoid falling into local optimum,and the randomness of Gaussian distribution is used to increase population diversity.The performance of the algorithm is evaluated by simulation experiments on seven benchmark functions and Wilcoxon rank sum.the experimental results indicate that the DLGOA algorithm realizes better robustness,the optimization accuracy and convergence speed are significantly improved.
作者
杨文珍
何庆
杜逆索
YANG Wen-zhen;HE Qing;DU Ni-suo(College of Big Data&Information Engineering,Guizhou University,Guiyang 550025,China;Guizhou Provincial Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2022年第2期247-253,共7页
Journal of Chinese Computer Systems
基金
贵州省科技计划项目重大专项项目(黔科合重大专项字[2018]3002,[2016]3022)资助
贵州省公共大数据重点实验室开放课题项目(2017BDKFJJ004)资助
贵州省教育厅青年科技人才成长项目(黔科合KY字[2016]124)资助
贵州大学培育项目(黔科合平台人才[2017]5788)资助。
关键词
蝗虫优化算法
自适应曲线函数
扰动因子
莱维飞行
grasshopper optimization algorithm
adaptive curve function
disturbance factors
levy flight
