TY - JOUR ID - 197815 TI - Optimized planar terahertz Yagi-Uda antenna using hybrid GSA-PSO optimization algorithm JO - International Conference on Aerospace Sciences and Aviation Technology JA - ASAT LA - en SN - 2090-0678 AU - Megahed, A AU - Mahmoud, K R AU - Shaker, A AD - MSC Student researcher, Department of Electronics, Communications, and Computers, Faculty of Engineering, Helwan University, Cairo, Egypt. AD - Associate Professor, Department of Electronics, Communications, and Computers, Faculty of Engineering, Helwan University, Cairo, Egypt. AD - Assistant Professor, Department of Electronics, Communications, and Computers, Faculty of Engineering, Helwan University, Cairo, Egypt. Y1 - 2019 PY - 2019 VL - 18 IS - 18 SP - 1 EP - 5 KW - gravitational search algorithm KW - gradient local algorithm KW - Genetic Algorithm KW - Optimization KW - Yagi-Uda DO - 10.1088/1757-899X/610/1/012092 N2 - 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. UR - https://asat.journals.ekb.eg/article_197815.html L1 - https://asat.journals.ekb.eg/article_197815_8326bbc55caf55472458b129e31b8304.pdf ER -