A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic Optimization

The aim of the paper is to introduce the hybrid technique for the multiobjective optimization of antennas. The goal of the antenna optimization is typically minimising the reflection coefficient through a frequency band. To minimize the energy consumption is essential consideration of energy efficie...

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Main Authors: A. Singh, R. M. Mehra, V. K. Pandey
Format: Article
Language:English
Published: Advanced Electromagnetics 2018-08-01
Series:Advanced Electromagnetics
Subjects:
Online Access:https://aemjournal.org/index.php/AEM/article/view/624
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spelling doaj-f04f4e5b83d74d90aa2c40b8423599ff2020-11-25T01:53:46ZengAdvanced ElectromagneticsAdvanced Electromagnetics2119-02752018-08-0173233410.7716/aem.v7i3.624624A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic OptimizationA. Singh0R. M. Mehra1V. K. Pandey2Sharda UniversitySharda UniversityNoida Institute of Engineering and TechnologyThe aim of the paper is to introduce the hybrid technique for the multiobjective optimization of antennas. The goal of the antenna optimization is typically minimising the reflection coefficient through a frequency band. To minimize the energy consumption is essential consideration of energy efficient transmission schemes that is used for the data transfer in wireless sensor networks. In our proposed work the efficient and low-cost multi objective technique CSGO (Cat Swarm based Genetic optimization) approach was used. The Cat Swarm Optimization approach is combined with genetic algorithm (GA) to optimize the bandwidth and return loss of the antenna. CSGO approach is to improve the Optimization efficiency and computational .This hybrid optimization approach will reduce the side lobe level and provide improvement in the Directivity. CSGO applied to the design of a miniaturized multiband antenna, showing better diversity and significant savings of overall optimization cost compared with the previously reported design methods.https://aemjournal.org/index.php/AEM/article/view/624Cat SwarmGenetic Optimization
collection DOAJ
language English
format Article
sources DOAJ
author A. Singh
R. M. Mehra
V. K. Pandey
spellingShingle A. Singh
R. M. Mehra
V. K. Pandey
A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic Optimization
Advanced Electromagnetics
Cat Swarm
Genetic Optimization
author_facet A. Singh
R. M. Mehra
V. K. Pandey
author_sort A. Singh
title A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic Optimization
title_short A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic Optimization
title_full A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic Optimization
title_fullStr A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic Optimization
title_full_unstemmed A Hybrid Approach for Antenna Optimization Using Cat Swarm based Genetic Optimization
title_sort hybrid approach for antenna optimization using cat swarm based genetic optimization
publisher Advanced Electromagnetics
series Advanced Electromagnetics
issn 2119-0275
publishDate 2018-08-01
description The aim of the paper is to introduce the hybrid technique for the multiobjective optimization of antennas. The goal of the antenna optimization is typically minimising the reflection coefficient through a frequency band. To minimize the energy consumption is essential consideration of energy efficient transmission schemes that is used for the data transfer in wireless sensor networks. In our proposed work the efficient and low-cost multi objective technique CSGO (Cat Swarm based Genetic optimization) approach was used. The Cat Swarm Optimization approach is combined with genetic algorithm (GA) to optimize the bandwidth and return loss of the antenna. CSGO approach is to improve the Optimization efficiency and computational .This hybrid optimization approach will reduce the side lobe level and provide improvement in the Directivity. CSGO applied to the design of a miniaturized multiband antenna, showing better diversity and significant savings of overall optimization cost compared with the previously reported design methods.
topic Cat Swarm
Genetic Optimization
url https://aemjournal.org/index.php/AEM/article/view/624
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