摘要
针对基本鲸鱼优化算法(whale optimization algorithm,WOA)求解复杂问题时存在解精度低、收敛速度慢和易陷入局部最优的缺点,提出了一种基于非线性调整控制参数和随机差分变异策略的改进鲸鱼优化算法(IWOA),设计了基于余弦函数非线性调整控制参数的策略,以协调算法的探索和开发能力;随机选择个体与当前个体进行差分变异产生新个体以增强群体的多样性,减小陷入局部最优的概率。对6个标准测试函数进行仿真实验,结果表明,IWOA的寻优精度和收敛速度均有明显的提高。
To solve low precision,slow convergence and easy to fall into local optimum for solving complex problems in the basic whale optimization algorithm（WOA）,an improved whale optimization algorithm（IWOA）based on nonlinear adjustment control parameter and stochastic differential mutation strategy is proposed.Firstly,a nonlinear adjustment control parameter strategy based on Cosine function is designed for coordinate exploration and exploitation.Then,a differential mutation strategy with the randomly selected individual and the current individual is introduced to enhance the diversity of population and reduce the probability of falling into a local optimum.Simulation experiments are conducted on six benchmark test functions.The results show that the IWOA has better performance in precision and convergence than other algorithms.
作者
覃溪
龙文
QIN Xi;LONG Wen(School of Lushan,Guangxi University of Science and Technology,Liuzhou,Guangxi 545616,China;Key Laboratory of Economics System Simulation of Guizhou Province,Guizhou University of Finance and Ecomomics,Guiyang 550025,China)
出处
《中国科技论文》
CAS
北大核心
2018年第8期937-942,共6页
China Sciencepaper
基金
国家自然科学基金资助项目(61463009)
广西高校中青年教师基础能力提升项目(2018KY0875)
贵州省高校科技拔尖人才支持计划项目(黔教合KY字[2017]070)
关键词
鲸鱼优化算法
随机差分变异
非线性调整控制参数
全局优化
whale optimization algorithm （WOA） stochastic differential mutation
nonlinear adjustment control parameter
global optimization
