Efficient global optimisation of microwave antennas based on a parallel surrogate model-assisted evolutionary algorithm
Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, w...
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| Published in | IET microwaves, antennas & propagation Vol. 13; no. 2; pp. 149 - 155 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
The Institution of Engineering and Technology
06.02.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-8725 1751-8733 1751-8733 |
| DOI | 10.1049/iet-map.2018.5009 |
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| Abstract | Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi-Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4-core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state-of-the-art surrogate model-assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases. |
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| AbstractList | Computational efficiency is a major challenge for evolutionary algorithm (EA)‐based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model‐assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model‐assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi‐Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4‐core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state‐of‐the‐art surrogate model‐assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases. |
| Author | Ali, Nazar Liu, Bo Akinsolu, Mobayode O Abd-Alhameed, Raed |
| Author_xml | – sequence: 1 givenname: Bo orcidid: 0000-0002-3093-4571 surname: Liu fullname: Liu, Bo email: liubo168@gmail.com organization: 2Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, UK – sequence: 2 givenname: Mobayode O surname: Akinsolu fullname: Akinsolu, Mobayode O organization: 2Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, UK – sequence: 3 givenname: Nazar surname: Ali fullname: Ali, Nazar organization: 3Department of Electrical and Computer Engineering, Khalifa University, Al Zafranah, Abu Dhabi, UAE – sequence: 4 givenname: Raed surname: Abd-Alhameed fullname: Abd-Alhameed, Raed organization: 4School of Electrical Engineering and Computer Science, University of Bradford, Richmond Road, Bradford, UK |
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| Cites_doi | 10.1109/TAP.2013.2283599 10.3233/JAE-150171 10.1109/TAP.2011.2109350 10.1016/j.advengsoft.2012.03.002 10.1007/978-1-4757-3799-8 10.1109/TMTT.2003.820891 10.1504/IJMMNO.2013.055204 10.1080/00401706.1987.10488205 10.1109/TAP.2014.2322880 10.1109/TMAG.2013.2239979 10.1007/978-3-642-39162-0 10.1016/j.asoc.2016.05.033 10.1109/LAWP.2014.2313698 10.1109/TEVC.2005.859463 10.1016/j.jocs.2015.11.004 10.2307/3001968 10.1109/TEVC.2013.2248012 10.1162/evco.1996.4.1.1 10.1023/A:1008306431147 10.1109/LAPC.2013.6711960 10.1109/TAP.2013.2283605 10.1016/B978-0-08-050684-5.50008-2 |
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| Keywords | efficient global optimisation microwave antennas parallel surrogate model-assisted evolutionary algorithm particle swarm optimisation evolutionary algorithm-based antenna optimisation methods optimisation efficiency computational efficiency surrogate model-assisted EA mathematical benchmark problems Yagi antenna arrays evolutionary computation PSADEA dielectric resonator antenna Yagi-Uda antenna antenna theory microwave antenna arrays dielectric resonator antennas parallel particle swarm optimisation electromagnetic simulations normal desktop 4-core workstation standard parallel differential evolution algorithm |
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| Snippet | Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive... Computational efficiency is a major challenge for evolutionary algorithm (EA)‐based antenna optimisation methods due to the computationally expensive... |
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| StartPage | 149 |
| SubjectTerms | antenna theory computational efficiency dielectric resonator antenna dielectric resonator antennas efficient global optimisation electromagnetic simulations evolutionary algorithm‐based antenna optimisation methods evolutionary computation mathematical benchmark problems microwave antenna arrays microwave antennas normal desktop 4‐core workstation optimisation efficiency parallel particle swarm optimisation parallel surrogate model‐assisted evolutionary algorithm particle swarm optimisation PSADEA Research Article standard parallel differential evolution algorithm surrogate model‐assisted EA Yagi antenna arrays Yagi‐Uda antenna |
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| Title | Efficient global optimisation of microwave antennas based on a parallel surrogate model-assisted evolutionary algorithm |
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