Optimized planar terahertz Yagi-Uda antenna using hybrid GSA-PSO optimization algorithm | ||||
| International Conference on Aerospace Sciences and Aviation Technology | ||||
| Article 93, Volume 18, Issue 18, April 2019, Page 1-5 PDF (390.29 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.1088/1757-899X/610/1/012092 | ||||
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| Authors | ||||
| A Megahed1; K R Mahmoud2; A Shaker3 | ||||
| 1MSC Student researcher, Department of Electronics, Communications, and Computers, Faculty of Engineering, Helwan University, Cairo, Egypt. | ||||
| 2Associate Professor, Department of Electronics, Communications, and Computers, Faculty of Engineering, Helwan University, Cairo, Egypt. | ||||
| 3Assistant Professor, Department of Electronics, Communications, and Computers, Faculty of Engineering, Helwan University, Cairo, Egypt. | ||||
| Abstract | ||||
| In this paper, a planar Yagi-Uda antenna design for Terahertz applications is presented and optimized. The lengths of Yagi-Uda antenna elements beside the spaces between directors’ elements are optimized seeking for obtaining high gain and minimum return loss simultaneously at 300 GHz. Thus, the directivity enhancement enables to overcome the high atmospheric attenuation influence that faces the high frequency range and limit its communication capabilities, and the return loss minimization enables to minimize the return loss to improve the impedance matching besides, reducing the mismatching losses. The Gravitational Search Optimization Algorithm with Particle Swarm Optimization algorithm (GSA-PSO) as a hybrid technique is applied on the antenna elements using an offline link between MATLAB program and Ansoft HFSS simulation program. To show the convergence capability of the GSA-PSO, the results are compared with those obtained by other algorithms such as gradient local algorithm and genetic global algorithm (GA). The optimized antenna showed high gain of 15.84 dB with a return loss of -45.27 dB compared to 15.4 dB and -15.6 dB for the conventional counterpart design, and with gain of 15.8 dB, and return loss of -21 dB for the genetic algorithm, and finally with 15.75 dB, and -28 dB for the gradient algorithm. Furthermore, the GSA-PSO algorithm improves the search capability by 30%, compared with the GA algorithm. | ||||
| Keywords | ||||
| gravitational search algorithm; gradient local algorithm; genetic algorithm; optimization; Yagi-Uda | ||||
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