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Linear array pattern synthesis using multi-objective optimization algorithm based on reference vectors

  • Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, non-dominated sorting genetic algorithm based on reference vectors (NSGA-II/BRV) is proposed to minimize the Peak Sidelobe Level (PSL) and the Average Broad Null Level (ABNL) for the pattern synthesis of linear array. Here, NSGA-II/BRV mainly focuses on the convergence and diversity of the obtained solutions for multi-objective problems. In order to get a better distribution and convergence of the obtained solutions, the members close to the uniformly distributed reference vectors will be selected. Compared with MOPSO, numerical examples demonstrate that NSGA-II/BRV applied to array pattern synthesis can obtains higher performance and better distribution of the final solutions.

Original languageEnglish
Title of host publication2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728106601
DOIs
StatePublished - May 2019
Event28th Wireless and Optical Communications Conference, WOCC 2019 - Beijing, China
Duration: 9 May 201910 May 2019

Publication series

Name2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings

Conference

Conference28th Wireless and Optical Communications Conference, WOCC 2019
Country/TerritoryChina
CityBeijing
Period9/05/1910/05/19

Keywords

  • Average Broad Null Level (ABNL)
  • Multi-objective problem
  • Pattern synthesis
  • Peak Sidelobe Level (PSL)
  • Reference vector

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