Computational Electromagnetic Modeling and Optimization Techniques to Enhance the Accuracy and Efficiency of a 2.45 GHz Pyramidal Horn Antenna by Newton–Raphson Method for IEMI Testing

This paper presents computational electromagnetic techniques that can be effectively used to improve the accuracy and efficiency of designing a 2.45[Formula: see text]GHz pyramidal horn antenna for high-power microwave applications. The design parameters were determined using analytical methods, and optimization techniques were conducted with CST Microwave Studio. The highest antenna gain achieved was 19.89[Formula: see text]dBi, which is comparable to the proposed numerical value of 20[Formula: see text]dBi. The return loss of the horn antenna was calculated numerically and found to be [Formula: see text] [Formula: see text]dB, with a resonance frequency of 2.45[Formula: see text]GHz. The Voltage Standing Wave Ratio (VSWR) was measured to be less than 1.5, while the time required for the analysis was 120[Formula: see text]min, and the storage needed was 17 GB. Using CST Microwave Studio and optimization techniques, the return losses for the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Interior Point Quadratic Newton (IPQN), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) were −29.61[Formula: see text]dB, −30.45[Formula: see text]dB, −26.2[Formula: see text]dB, and −30.24[Formula: see text]dB, respectively. The corresponding VSWR values for GA, PSO, IPQN, and CMA-ES were 1.491, 1.42, 1.141, and 1.13. The gains for the same methods were found to be 20.3[Formula: see text]dBi, 20.2[Formula: see text]dBi, 19.8[Formula: see text]dBi, and 20[Formula: see text]dBi, respectively. This demonstrates that CST Microwave Studio effectively optimizes return loss, VSWR, and gain. In comparison to the GA optimizer, which required 3876[Formula: see text]min and 29[Formula: see text]s with a storage capacity of 23.4[Formula: see text]GB to perform 145 evaluations, the PSO optimizer took 7055[Formula: see text]min and 5[Formula: see text]s to complete 241 evaluations, needing 36[Formula: see text]GB of storage. Additionally, compared to CMA-ES, which utilized 26[Formula: see text]GB of storage to finish 186 evaluations in 6131[Formula: see text]min and 42 s, IPQN required only 155[Formula: see text]min and 44[Formula: see text]s with 24.6[Formula: see text]GB of storage to complete 3729 evaluations.