当前位置: X-MOL 学术Knowl. Based Syst. › 论文详情
Improved Binary Grey Wolf Optimizer and Its application for feature selection
Knowledge-Based Systems ( IF 5.101 ) Pub Date : 2020-03-09 , DOI: 10.1016/j.knosys.2020.105746
Pei Hu; Jeng-Shyang Pan; Shu-Chuan Chu

Grey Wolf Optimizer (GWO) is a new swarm intelligence algorithm mimicking the behaviors of grey wolves. Its abilities include fast convergence, simplicity and easy realization. It has been proved its superior performance and widely used to optimize the continuous applications, such as, cluster analysis, engineering problem, training neural network and etc. However, there are still some binary problems to optimize in the real world. Since binary can only be taken from values of 0 or 1, the standard GWO is not suitable for the problems of discretization. Binary Grey Wolf Optimizer (BGWO) extends the application of the GWO algorithm and is applied to binary optimization issues. In the position updating equations of BGWO, the a parameter controls the values of A and D, and influences algorithmic exploration and exploitation. This paper analyzes the range of values of AD under binary condition and proposes a new updating equation for the a parameter to balance the abilities of global search and local search. Transfer function is an important part of BGWO, which is essential for mapping the continuous value to binary one. This paper includes five transfer functions and focuses on improving their solution quality. Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed. It successfully implements feature selection in the UCI datasets and acquires low classification errors with few features.
更新日期:2020-03-09

 

全部期刊列表>>
智控未来
聚焦商业经济政治法律
跟Nature、Science文章学绘图
控制与机器人
招募海内外科研人才,上自然官网
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
湖南大学化学化工学院刘松
上海有机所
李旸
南方科技大学
西湖大学
X-MOL
支志明
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug