Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning

Multi-objective optimization (MO) is an important topic in contemporary antenna design. Due to the reliance on computationally-expensive electromagnetic (EM) simulations, the use of conventional algorithms is prohibitive. These costs can be reduced by appropriate algorithmic tools involving surrogate modeling and soft computing methods. This study introduces an innovative artificial intelligence (AI)-based approach to antenna MO. Our algorithm is a machine learning (ML) procedure employing artificial neural network models. In each iteration, multiple infill vectors are produced, using Pareto ranking of the candidate solution set produced by a multi-objective evolutionary algorithm. The full-wave simulation results acquired for all infill points are incorporated into the dataset to refine the metamodel. Termination of the procedure is based on a comparison of non-dominated solutions obtained in subsequent iterations. Additional reduction of the expenses is enabled through the use of multi-resolution electromagnetic simulations. The presented methodology has been extensively demonstrated with the help of four planar devices, including broadband monopoles and a quasi-Yagi antenna. As shown, the average cost of MO is equivalent to approximately two hundred high-fidelity EM analyses. In absolute terms, 40% of relative speedup is achieved due to variable-fidelity modeling, and almost 90% savings over the one-shot approach. Comparative experiments indicate that the improved computational efficiency of the presented framework is not detrimental to reliability. Consequently, the introduced algorithm can be regarded a feasible alternative to the current MO methodologies for antennas, especially when computational budget is a critical constraint.