A Linear Decreasing Inertia Weight Particle Swarm Optimization Base on a $K$-means Clustering Chaotic Sampling for Antenna Design

Peng-Fei Qin, Wen-Hao Li, Dong Wang, Guanhua Huang, W. Fan, C. Sim
{"title":"A Linear Decreasing Inertia Weight Particle Swarm Optimization Base on a $K$-means Clustering Chaotic Sampling for Antenna Design","authors":"Peng-Fei Qin, Wen-Hao Li, Dong Wang, Guanhua Huang, W. Fan, C. Sim","doi":"10.1109/CSRSWTC56224.2022.10098311","DOIUrl":null,"url":null,"abstract":"This paper presents a $K$-means clustering chaotic sampling linear decreasing inertia weight particle swarm optimization (KCS-LDIWPSO) method for antenna fast design application. In this proposed method, a $K$-means clustering chaotic sampling method is used to obtain the initial particle swarm. The linear decreasing inertia weight particle swarm optimization method updates the particles to improve the optimization capability of the particle swarm optimization method. The proposed method is verified through optimization of a slotted patch antenna. The results show that the proposed method finds a required antenna with less simulation cost and computational time than other optimization methods.","PeriodicalId":198168,"journal":{"name":"2022 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSRSWTC56224.2022.10098311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper presents a $K$-means clustering chaotic sampling linear decreasing inertia weight particle swarm optimization (KCS-LDIWPSO) method for antenna fast design application. In this proposed method, a $K$-means clustering chaotic sampling method is used to obtain the initial particle swarm. The linear decreasing inertia weight particle swarm optimization method updates the particles to improve the optimization capability of the particle swarm optimization method. The proposed method is verified through optimization of a slotted patch antenna. The results show that the proposed method finds a required antenna with less simulation cost and computational time than other optimization methods.
基于K -均值聚类混沌采样的线性减小惯性权粒子群优化天线设计
提出了一种用于天线快速设计的$K$均值聚类混沌采样线性递减惯性权粒子群优化方法。该方法采用$K$均值聚类混沌采样方法获得初始粒子群。线性减小惯性权的粒子群优化方法对粒子进行更新,提高了粒子群优化方法的优化能力。通过对一个开槽贴片天线的优化,验证了该方法的有效性。结果表明,与其他优化方法相比,该方法能够以更少的仿真成本和计算时间找到所需的天线。
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