{"title":"基于多目标优化的稀疏天线阵综合","authors":"L. Pappula, D. Ghosh","doi":"10.1109/AEMC.2013.7045039","DOIUrl":null,"url":null,"abstract":"The process of sparse antenna array synthesis involves the simultaneous minimization of the number of mutually conflicting parameters, such as peak sidelobe level and first null beam width. This necessitates the development of a multi objective optimization process which will provide the best compromised solution based on the application at hand. In this paper multi-objective optimization is achieved using the non-dominating sorting genetic algorithm of NSGA-II. This approach yields much more improved results as compared to single objective optimization approach and at the same time it offers flexibility in choosing the solution based on the Pareto front.","PeriodicalId":169237,"journal":{"name":"2013 IEEE Applied Electromagnetics Conference (AEMC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Sparse antenna array synthesis using multi-objective optimization\",\"authors\":\"L. Pappula, D. Ghosh\",\"doi\":\"10.1109/AEMC.2013.7045039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The process of sparse antenna array synthesis involves the simultaneous minimization of the number of mutually conflicting parameters, such as peak sidelobe level and first null beam width. This necessitates the development of a multi objective optimization process which will provide the best compromised solution based on the application at hand. In this paper multi-objective optimization is achieved using the non-dominating sorting genetic algorithm of NSGA-II. This approach yields much more improved results as compared to single objective optimization approach and at the same time it offers flexibility in choosing the solution based on the Pareto front.\",\"PeriodicalId\":169237,\"journal\":{\"name\":\"2013 IEEE Applied Electromagnetics Conference (AEMC)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Applied Electromagnetics Conference (AEMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEMC.2013.7045039\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Applied Electromagnetics Conference (AEMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEMC.2013.7045039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sparse antenna array synthesis using multi-objective optimization
The process of sparse antenna array synthesis involves the simultaneous minimization of the number of mutually conflicting parameters, such as peak sidelobe level and first null beam width. This necessitates the development of a multi objective optimization process which will provide the best compromised solution based on the application at hand. In this paper multi-objective optimization is achieved using the non-dominating sorting genetic algorithm of NSGA-II. This approach yields much more improved results as compared to single objective optimization approach and at the same time it offers flexibility in choosing the solution based on the Pareto front.