Comparative study of broadcasting antenna array optimization using evolutionary algorithms

Pavlos I. Lazaridis, Emmanouil N. Tziris, Zaharias D. Zaharis, Thomas D. Xenos, Violeta Holmes, John P. Cosmas, Ian Glover

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Broadcasting antenna array optimized design involves gain maximization, main lobe down-tilting and null filling. In this study some of the most powerful evolutionary optimization algorithms are applied to this challenging problem: Differential Evolution, Particle Swarm, Invasive Weed, Adaptive Invasive Weed, and the Taguchi method. Evolutionary algorithms use a random search approach together with mechanisms inspired by biological evolution in order to iteratively improve the precision of randomly obtained solutions. Evolutionary algorithms are shown to require very substantial computational resources due to their random search nature. However, they are also very robust in finding a quasi-optimum solution by optimizing an appropriate fitness function. It is demonstrated that the algorithm producing the best fitness, and thus the best solution to the antenna problem, is Invasive Weed Optimization (IWO), followed by Particle Swarm Optimization (PSO) and Differential Evolution (DE), in second place and with similar results.

LanguageEnglish
Title of host publication2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1299-1301
Number of pages3
ISBN (Electronic)9781467388016
DOIs
Publication statusPublished - 20 Oct 2016
Event2016 URSI Asia-Pacific Radio Science Conference - Grand Hilton Seoul Hotel, Seoul, Korea, Republic of
Duration: 21 Aug 201625 Aug 2016
https://www.ursi.org/content/AP-RASC/AP-RASC2016/URSI_AP-RASC_2016_Program.pdf (Link to Conference Programme )

Conference

Conference2016 URSI Asia-Pacific Radio Science Conference
Abbreviated titleURSI AP-RASC 2016
CountryKorea, Republic of
CitySeoul
Period21/08/1625/08/16
Internet address

Fingerprint

Broadcasting antennas
broadcasting
antenna arrays
Antenna arrays
Evolutionary algorithms
optimization
fitness
Taguchi methods
biological evolution
Particle swarm optimization (PSO)
lobes
Antennas
resources
antennas

Cite this

Lazaridis, P. I., Tziris, E. N., Zaharis, Z. D., Xenos, T. D., Holmes, V., Cosmas, J. P., & Glover, I. (2016). Comparative study of broadcasting antenna array optimization using evolutionary algorithms. In 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016 (pp. 1299-1301). [7601166] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URSIAP-RASC.2016.7601166
Lazaridis, Pavlos I. ; Tziris, Emmanouil N. ; Zaharis, Zaharias D. ; Xenos, Thomas D. ; Holmes, Violeta ; Cosmas, John P. ; Glover, Ian. / Comparative study of broadcasting antenna array optimization using evolutionary algorithms. 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1299-1301
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Lazaridis, PI, Tziris, EN, Zaharis, ZD, Xenos, TD, Holmes, V, Cosmas, JP & Glover, I 2016, Comparative study of broadcasting antenna array optimization using evolutionary algorithms. in 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016., 7601166, Institute of Electrical and Electronics Engineers Inc., pp. 1299-1301, 2016 URSI Asia-Pacific Radio Science Conference, Seoul, Korea, Republic of, 21/08/16. https://doi.org/10.1109/URSIAP-RASC.2016.7601166

Comparative study of broadcasting antenna array optimization using evolutionary algorithms. / Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Holmes, Violeta; Cosmas, John P.; Glover, Ian.

2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1299-1301 7601166.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Tziris, Emmanouil N.

AU - Zaharis, Zaharias D.

AU - Xenos, Thomas D.

AU - Holmes, Violeta

AU - Cosmas, John P.

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N2 - Broadcasting antenna array optimized design involves gain maximization, main lobe down-tilting and null filling. In this study some of the most powerful evolutionary optimization algorithms are applied to this challenging problem: Differential Evolution, Particle Swarm, Invasive Weed, Adaptive Invasive Weed, and the Taguchi method. Evolutionary algorithms use a random search approach together with mechanisms inspired by biological evolution in order to iteratively improve the precision of randomly obtained solutions. Evolutionary algorithms are shown to require very substantial computational resources due to their random search nature. However, they are also very robust in finding a quasi-optimum solution by optimizing an appropriate fitness function. It is demonstrated that the algorithm producing the best fitness, and thus the best solution to the antenna problem, is Invasive Weed Optimization (IWO), followed by Particle Swarm Optimization (PSO) and Differential Evolution (DE), in second place and with similar results.

AB - Broadcasting antenna array optimized design involves gain maximization, main lobe down-tilting and null filling. In this study some of the most powerful evolutionary optimization algorithms are applied to this challenging problem: Differential Evolution, Particle Swarm, Invasive Weed, Adaptive Invasive Weed, and the Taguchi method. Evolutionary algorithms use a random search approach together with mechanisms inspired by biological evolution in order to iteratively improve the precision of randomly obtained solutions. Evolutionary algorithms are shown to require very substantial computational resources due to their random search nature. However, they are also very robust in finding a quasi-optimum solution by optimizing an appropriate fitness function. It is demonstrated that the algorithm producing the best fitness, and thus the best solution to the antenna problem, is Invasive Weed Optimization (IWO), followed by Particle Swarm Optimization (PSO) and Differential Evolution (DE), in second place and with similar results.

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Lazaridis PI, Tziris EN, Zaharis ZD, Xenos TD, Holmes V, Cosmas JP et al. Comparative study of broadcasting antenna array optimization using evolutionary algorithms. In 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1299-1301. 7601166 https://doi.org/10.1109/URSIAP-RASC.2016.7601166