An optimized surrogate model using differential evolution algorithm for computing parameters of antennas
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CitationÜstün, D. Toktaş, F. ve Toktaş, A. (2022). An optimized surrogate model using differential evolution algorithm for computing parameters of antennas. International Journal of Numerical Modelling-Electronic Networks Devices and Fields, 35,2, 1-12.
In this study, a method based on surrogate model (SM) for computational analysis of antenna parameters such as the resonant frequency (RF) and bandwidth (BW) is presented. Moreover, it is attempted to optimize the SM using evolutionary optimization algorithms in order to further improve the accuracy of the SM. In the conventional computational approaches, the weighting vectors of the SM have been analytically determined. We have optimally achieved the weighting vectors of the SM through differential evolution (DE) and particle swarm optimization (PSO) algorithms. The capabilities of the algorithms are hereby compared with each other. The methodology is applied to the analysis of rectangular microstrip antenna (RMA), including a number of 33 measured RMAs with different geometrical and electrical parameters. From the total number of RMAs, 27 and 6 RMAs are, respectively, used in the construction and the test of the SM. Furthermore, the SM is verified through a comparison with the literature in terms of total absolute errors (TAEs). The results show that the SM with DE computes the most accurate RF and BW with the TAEs of 0.0099 GHz and 0.131%, respectively. The accuracy of the SM is further raised by 78%, thanks to the optimization of SM with DE. Therefore, a novel computational analysis method based on SM is implemented to computation of an antenna parameter with higher accuracy, and SM is successfully optimized by DE. The proposed method is able to easily implement to the stringent engineering problems based on simulated or measured data for computer-aided design (CAD).