摘要
针对传统鲸鱼优化算法收敛速度慢、易陷入局部最优等问题,提出一种基于混合策略改进的鲸鱼优化算法。首先,引入非线性调整策略改进收敛因子,平衡算法的全局探索与局部开发能力并加快算法收敛速度;然后,将自适应权重系数引入鲸鱼位置更新式中,从而提高算法的寻优精度;最后,结合人工蜂群算法的limit阈值思想,使算法能够有效跳出局部最优,改善算法早熟收敛现象。通过对14个基准测试函数在不同维度上的仿真实验表明,改进算法具有较高的寻优精度和较快的收敛速度。
In order to solve the disadvantage of the traditional whale optimization algorithm,which is slow convergence and easy to fall into local optimum,this paper proposed a mixed strategy based whale optimization algorithm. Firstly,it introduced the nonlinear adjustment strategy to modify the convergence factor,balanced the exploration and exploitation capability and accelerated the convergence speed. Then,it introduced an adaptive weighted coefficient into the position update formula of whales to improve the search precision of the algorithm. Finally,it combined the limit threshold idea of artificial bee colony algorithm to effectively jump out of the local optimum and prevent premature convergence. The results show that the proposed algorithm has better search precision and convergence speed through experiments on different dimensions of 14 benchmark functions.
作者
何庆
魏康园
徐钦帅
He Qing;Wei Kangyuan;Xu Qinshuai(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
《计算机应用研究》
CSCD
北大核心
2019年第12期3647-3651,3665,共6页
Application Research of Computers
基金
贵州省科技计划项目重大专项资助项目(黔科合重大专项字[2018]3002)
贵州省公共大数据重点实验室开放课题(2017BDKFJJ004)
贵州省教育厅青年科技人才成长项目(黔科合KY字[2016]124)
贵州大学培育项目(黔科合平台人才[2017]5788)
关键词
鲸鱼优化算法
非线性收敛因子
自适应权重系数
limit阈值
whale optimization algorithm
nonlinear convergence factor
adaptive weighted coefficient
limit threshold
