PSO、GA-PSO和蚁群算法在印刷天线设计优化中的应用研究

2017 IEEE Applied Electromagnetics Conference (AEMC) Pub Date : 2017-12-01 DOI:10.1109/AEMC.2017.8325661

M. Akila, P. Anusha, M. Sindhu, K. Selvan

{"title":"PSO、GA-PSO和蚁群算法在印刷天线设计优化中的应用研究","authors":"M. Akila, P. Anusha, M. Sindhu, K. Selvan","doi":"10.1109/AEMC.2017.8325661","DOIUrl":null,"url":null,"abstract":"Particle swarm optimization (PSO), Genetic Algorithm (GA) and Ant colony optimization (ACO) are widely used algorithms for optimization problems in electromagnetics. In this paper ACO, PSO and GA based PSO (GA-PSO) are implemented and compared for optimizing the (i) feed location of a simple patch and (ii) element spacing in a linear array to obtain the desired beamwidth. For the aforementioned problems, ACO seems to offer solutions with lesser computational time.","PeriodicalId":397541,"journal":{"name":"2017 IEEE Applied Electromagnetics Conference (AEMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Examination of PSO, GA-PSO and ACO algorithms for the design optimization of printed antennas\",\"authors\":\"M. Akila, P. Anusha, M. Sindhu, K. Selvan\",\"doi\":\"10.1109/AEMC.2017.8325661\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Particle swarm optimization (PSO), Genetic Algorithm (GA) and Ant colony optimization (ACO) are widely used algorithms for optimization problems in electromagnetics. In this paper ACO, PSO and GA based PSO (GA-PSO) are implemented and compared for optimizing the (i) feed location of a simple patch and (ii) element spacing in a linear array to obtain the desired beamwidth. For the aforementioned problems, ACO seems to offer solutions with lesser computational time.\",\"PeriodicalId\":397541,\"journal\":{\"name\":\"2017 IEEE Applied Electromagnetics Conference (AEMC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Applied Electromagnetics Conference (AEMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEMC.2017.8325661\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Applied Electromagnetics Conference (AEMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEMC.2017.8325661","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

粒子群算法(PSO)、遗传算法(GA)和蚁群算法(ACO)是求解电磁学优化问题的常用算法。本文对蚁群算法、粒子群算法和基于遗传算法的粒子群算法(GA-PSO)进行了实现和比较，以优化(i)简单贴片的馈电位置和(ii)线性阵列中的元素间距，以获得所需的波束宽度。对于上述问题，蚁群算法似乎提供了更少计算时间的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Examination of PSO, GA-PSO and ACO algorithms for the design optimization of printed antennas

Particle swarm optimization (PSO), Genetic Algorithm (GA) and Ant colony optimization (ACO) are widely used algorithms for optimization problems in electromagnetics. In this paper ACO, PSO and GA based PSO (GA-PSO) are implemented and compared for optimizing the (i) feed location of a simple patch and (ii) element spacing in a linear array to obtain the desired beamwidth. For the aforementioned problems, ACO seems to offer solutions with lesser computational time.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 IEEE Applied Electromagnetics Conference (AEMC)

自引率

0.00%

发文量