摘要
针对鲸鱼优化算法在面对复杂优化问题时,存在易陷入局部最优和收敛精度低等缺点,在原始鲸鱼算法的基础上,提出了信息熵的改进鲸鱼优化算法.信息熵本身是一种不确定的度量,利用信息熵在路径选择时调控鲸鱼搜索的范围,克服基本鲸鱼优化算法的不足,使算法的全局收敛速度得到提高.通过选取6个标准测试函数进行仿真实验,对改进鲸鱼优化算法、基本鲸鱼优化算法、粒子群算法进行比较,数据结果表明改进鲸鱼算法在处理高维复杂组合优化问题上的可行性与有效性.
In view of the shortcomings of whale algorithm(WOA)in the face of complex optimization problems,such as easily falling into local optimum and low convergence accuracy,the information entropy is introduced on the basis of original whale algorithm.Information entropy is a measure of uncertainty.In order to overcome the shortcomings of the basic whale optimization algorithm,the information entropy is used to adjust the range of whale searches for path selection,and the global convergence speed of the algorithm is improved.By choosing six standard test functions of simulation experiments,the improved whale optimization algorithm,basic whale optimization algorithm and particle swarm optimization algorithm are compared.The results show that the improved whale algorithm is feasible and effective in dealing with high-dimensional complex combinatorial optimization problems.
作者
刘历波
赵廷廷
李彦苍
王斌
LIU Li-bo;ZHAO Ting-ting;LI Yan-cang;WANG Bin(College of Civil Engineering,Hebei University of Engineering,Handan 056038,China)
出处
《数学的实践与认识》
北大核心
2020年第2期211-219,共9页
Mathematics in Practice and Theory
基金
国家自然基金青年科学基金(51708317)
河北省建设科技研究计划项目(2017-146).
关键词
启发式优化算法
鲸鱼优化算法
信息熵
组合优化
函数优化
heuristic optimization algorithm
whale algorithm
information entropy
combinatorial optimization
function optimization
