Improved-Efficacy EM-Driven Optimization of Antenna Structures Using Adaptive Design Specifications and Variable-Resolution Models

Optimization-driven parameter tuning is an essential step in the design of antenna systems. Although in many cases, it is still conducted through parametric studies, rigorous numerical methods become a necessity if truly optimum designs are sought for, and the problem intricacies (number of variables, multiple goals, and constraints) make the interactive approaches insufficient. The two practical considerations of electromagnetic (EM)-driven optimization are reliability and computational cost. Repetitive EM simulations may incur unmanageable expenses, whereas the lack of a decent starting point or objective function multimodality may prevent the numerical procedures (especially the local ones) from identifying satisfactory designs. In pursuit of reliability improvements, a design specification adjustment procedure has been recently proposed that improves the immunity of local search procedures to poor starting points, where the objective function is modified by relocating the design goals (e.g., center frequencies) closer to the actual operating parameters of the antenna at the current design, to make them attainable through local search. The goals are then gradually adjusted and converged to the original targets toward the end of the optimization process. In this article, we introduce an algorithm for reliable and low-cost antenna tuning that capitalizes on the specification management scheme while embedding it in variable-resolution optimization framework. In our approach, the EM model fidelity is adaptively adjusted based on the misalignment between the actual and target operating conditions, as well as the convergence status of the algorithm. By initiating the search process from the lowest fidelity model (gradually dialed up to the highest fidelity one), considerable computational savings of almost 60% can be achieved, with respect to the single-fidelity procedure. The speedup is possible without compromising reliability of the optimization process, as demonstrated using three examples of microstrip antennas, designed under different and challenging scenarios.