Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models

Array thinning is a common discrete-valued combinatorial optimization problem. Evolutionary algorithms are suitable techniques for above-mentioned problem. Biogeography-Based Optimization (BBO), which is inspired by the science of biogeography, is a stochastic population-based evolutionary algorithm...

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Main Authors: Sotirios K. Goudos, John N. Sahalos
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2016/5359298
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author Sotirios K. Goudos
John N. Sahalos
author_facet Sotirios K. Goudos
John N. Sahalos
author_sort Sotirios K. Goudos
collection DOAJ
description Array thinning is a common discrete-valued combinatorial optimization problem. Evolutionary algorithms are suitable techniques for above-mentioned problem. Biogeography-Based Optimization (BBO), which is inspired by the science of biogeography, is a stochastic population-based evolutionary algorithm (EA). The original BBO uses a linear migration model to describe how species migrate from one island to another. Other nonlinear migration models have been proposed in the literature. In this paper, we apply BBO with four different migration models to five different large array design cases. Additionally we compare results with five other popular algorithms. The problems dimensions range from 150 to 300. The results show that BBO with sinusoidal migration model generally performs better than the other algorithms. However, these results are considered to be indicative and do not generally apply to all optimization problems in antenna design.
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series International Journal of Antennas and Propagation
spelling doaj-art-8b1b58f19d2544038ee7ae42fd88bd302025-02-03T06:08:00ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772016-01-01201610.1155/2016/53592985359298Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration ModelsSotirios K. Goudos0John N. Sahalos1Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, University of Nicosia, Nicosia, CyprusArray thinning is a common discrete-valued combinatorial optimization problem. Evolutionary algorithms are suitable techniques for above-mentioned problem. Biogeography-Based Optimization (BBO), which is inspired by the science of biogeography, is a stochastic population-based evolutionary algorithm (EA). The original BBO uses a linear migration model to describe how species migrate from one island to another. Other nonlinear migration models have been proposed in the literature. In this paper, we apply BBO with four different migration models to five different large array design cases. Additionally we compare results with five other popular algorithms. The problems dimensions range from 150 to 300. The results show that BBO with sinusoidal migration model generally performs better than the other algorithms. However, these results are considered to be indicative and do not generally apply to all optimization problems in antenna design.http://dx.doi.org/10.1155/2016/5359298
spellingShingle Sotirios K. Goudos
John N. Sahalos
Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models
International Journal of Antennas and Propagation
title Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models
title_full Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models
title_fullStr Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models
title_full_unstemmed Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models
title_short Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models
title_sort design of large thinned arrays using different biogeography based optimization migration models
url http://dx.doi.org/10.1155/2016/5359298
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