Dual-Band Planar Microstrip Monopole Antenna Design Using Multi-Objective Hybrid Optimization Algorithm

• Published in: Photonics & Electromagnetics Research Symposium (Online) pp. 467 - 475
• Main Authors: Hosseini, V., Shapour, F., Pinho, P., Farhang, Y., Majidzadeh, K., Ghobadi, Ch, Nourinia, J., Barshandeh, S., Shokri, M., Amiri, Zh, Jalilirad, M., Kaboutari, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 03.07.2023
• Subjects: Dual band; Electromagnetics; Metaheuristics; Microstrip; Microstrip antennas; Optimization; Wireless LAN
• ISSN: 2831-5804
• DOI: 10.1109/PIERS59004.2023.10221360

This research uses a new proposed multi-objective optimization algorithm that utilizes an untested combined chaotic map to integrate Customized Mutated PSO (CM-PSO) with a Modified Genetic Algorithm (MGA) to design an antenna with specific electromagnetic characteristics. The hybrid approach achieves preferred outcomes faster than PSO, CM-PSO, GA, and MGA by avoiding being trapped in local minimums. The proposed Metaheuristic Algorithm (MA) functionality has been authenticated successfully using Benchmark Functions (BFs) like Rastrigin Function (RaF), Ackley Function (AF), Rosenbrock Function (RoF), and Booth Function (BoF). Finally, the validity of the offered approach for electromagnetic applications is demonstrated by optimizing a dual-band planar microstrip monopole antenna with a simple structure, such that its optimized S_{11} be less than -10 dB at two frequency bands encompasses 2.4 to 2.484 and 5.15 to 5.825 GHz for Wireless Local Area Network (WLAN) with IEEE 802.11 standards. The proposed algorithm allows the optimization criteria to be customized. The optimization algorithm developed in MATLAB is used to determine the necessary parameter adjustments in order to achieve expected frequency bands using CM-PSO or an innovative MGA, while high-frequency and electromagnetic simulations are performed using Computer Simulation Technology (CST) Studio Suite. The dimensions of the proposed antenna's elements are critical input parameters of the algorithm, named decision variables.