中文版 | English
题名

BSO20: efficient brain storm optimization for real-parameter numerical optimization

作者
通讯作者Luo,Wenjian
发表日期
2021-10-01
DOI
发表期刊
ISSN
2199-4536
EISSN
2198-6053
卷号7期号:5页码:2415-2436
摘要
Brain storm optimization (BSO) is an emerging global optimization algorithm. The primary idea is to divide the population into different clusters, and offspring are generated within a cluster or between two clusters. However, the problems of inefficient clustering strategy and insufficient exploration exist in BSO. In this paper, a novel and efficient BSO is proposed, called BSO20 (proposed in 2020). BSO20 pays attention to both the clustering strategy and the mutation strategy. First, we propose a hybrid clustering strategy, which combines two clustering strategies, i.e., nearest-better clustering and random grouping strategy. The size of the subpopulation clustered by two strategies is dynamically adjusted as the population evolves. Second, a modified mutation strategy is used in BSO20 to share information within a cluster or among multiple clusters to enhance the ability of exploration. BSO20 is tested on the problems of the 2017 IEEE Congress on Evolutionary Computation competition on real parameter numerical optimization. BSO20 is compared with several variants of BSO and two variants of particle swarm optimization, and the experimental results show that BSO20 is competitive.
关键词
相关链接[Scopus记录]
收录类别
语种
英语
学校署名
其他
资助项目
National Key Research and Development Program of China[2020YFB2104003];National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[61573327];
WOS记录号
WOS:000662814700002
Scopus记录号
2-s2.0-85134038702
来源库
Scopus
引用统计
被引频次[WOS]:8
成果类型期刊论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/406625
专题工学院_计算机科学与工程系
作者单位
1.School of Computer Science and Technology,University of Science and Technology of China,Hefei,Anhui,230027,China
2.School of Computer Science and Technology,Harbin Institute of Technology,Shenzhen,Guangdong,518055,China
3.School of Computer Science,Shaanxi Normal University,Xi’an,710119,China
4.Shenzhen Key Lab of Computational Intelligence,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
推荐引用方式
GB/T 7714
Xu,Peilan,Luo,Wenjian,Lin,Xin,et al. BSO20: efficient brain storm optimization for real-parameter numerical optimization[J]. Complex & Intelligent Systems,2021,7(5):2415-2436.
APA
Xu,Peilan,Luo,Wenjian,Lin,Xin,Cheng,Shi,&Shi,Yuhui.(2021).BSO20: efficient brain storm optimization for real-parameter numerical optimization.Complex & Intelligent Systems,7(5),2415-2436.
MLA
Xu,Peilan,et al."BSO20: efficient brain storm optimization for real-parameter numerical optimization".Complex & Intelligent Systems 7.5(2021):2415-2436.
条目包含的文件
条目无相关文件。
个性服务
原文链接
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
导出为Excel格式
导出为Csv格式
Altmetrics Score
谷歌学术
谷歌学术中相似的文章
[Xu,Peilan]的文章
[Luo,Wenjian]的文章
[Lin,Xin]的文章
百度学术
百度学术中相似的文章
[Xu,Peilan]的文章
[Luo,Wenjian]的文章
[Lin,Xin]的文章
必应学术
必应学术中相似的文章
[Xu,Peilan]的文章
[Luo,Wenjian]的文章
[Lin,Xin]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
[发表评论/异议/意见]
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。