Peng-Fei Qin, Wen-Hao Li, Dong Wang, Guanhua Huang, W. Fan, C. Sim
{"title":"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","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":"{\"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}","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}
A Linear Decreasing Inertia Weight Particle Swarm Optimization Base on a $K$-means Clustering Chaotic Sampling for Antenna Design
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.