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Title: | Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers |
Authors: | Boursianis, Achilles D. Papadopoulou, Maria S. Salucci, Marco Polo, Alessandro Sarigiannidis, Panagiotis Psannis, Kostas E. Mirjalili, Seyedali Koulouridis, Stavros Goudos, Sotirios K. |
Type: | Article |
Subjects: | FRASCATI::Engineering and technology FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering |
Keywords: | antenna design aperture-coupled antenna meta-heuristics nature-inspired algorithms optimization technique swarm intelligence grey wolf optimizer whale optimization algorithm salp swarm algorithm |
Issue Date: | 2021 |
Source: | Applied Sciences |
Volume: | 11 |
Issue: | 18 |
First Page: | 8330 |
Abstract: | Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands |
URI: | https://doi.org/10.3390/app11188330 https://ruomo.lib.uom.gr/handle/7000/1534 |
ISSN: | 2076-3417 |
Other Identifiers: | 10.3390/app11188330 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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applsci-11-08330-v2.pdf | applsci-11-08330-v2.pdf | 11,72 MB | Adobe PDF | View/Open |
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