Pattern Synthesis of Antenna Arrays Using Dynamic Cooperative Grey Wolf Optimizer Algorithm

Yan Liu, Yaming Zhang, S. Gao
{"title":"Pattern Synthesis of Antenna Arrays Using Dynamic Cooperative Grey Wolf Optimizer Algorithm","authors":"Yan Liu, Yaming Zhang, S. Gao","doi":"10.1109/ICEIEC49280.2020.9152282","DOIUrl":null,"url":null,"abstract":"In this paper, a novel method for pattern synthesis of antenna arrays based on dynamic cooperative gray wolf optimizer algorithm is proposed. By introducing the dynamic cooperative weight factor, positions of the three leader wolves are weighted, and then the positions of next generation are updated. The improved algorithm emphasizes the leading role of the leader wolf, but also does not neglect the cooperation between the leadership wolves. Global optimal and local optimal can be well balanced and converge to the optimal solution quickly and effectively. The proposed algorithm has been successfully used to reduce the sidelobe level and form deep null steerings at designated directions which meets the design requirements.","PeriodicalId":352285,"journal":{"name":"2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC)","volume":"532 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEIEC49280.2020.9152282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In this paper, a novel method for pattern synthesis of antenna arrays based on dynamic cooperative gray wolf optimizer algorithm is proposed. By introducing the dynamic cooperative weight factor, positions of the three leader wolves are weighted, and then the positions of next generation are updated. The improved algorithm emphasizes the leading role of the leader wolf, but also does not neglect the cooperation between the leadership wolves. Global optimal and local optimal can be well balanced and converge to the optimal solution quickly and effectively. The proposed algorithm has been successfully used to reduce the sidelobe level and form deep null steerings at designated directions which meets the design requirements.
基于动态协同灰狼优化算法的天线阵方向图综合
提出了一种基于动态协同灰狼优化算法的天线阵方向图综合新方法。通过引入动态合作权重因子,对3头狼的位置进行加权,进而更新下一代狼的位置。改进算法在强调领导狼的领导作用的同时,也不忽视领导狼之间的合作。该算法能够很好地平衡全局最优和局部最优,并快速有效地收敛到最优解。该算法成功地降低了副瓣电平,并在指定方向形成了深零转向,满足了设计要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信