Accelerated Gradient-Based Optimization of Antenna Structures Using Multifidelity Simulations and Convergence-Based Model Management Scheme

The importance of numerical optimization has been steadily growing in the design of contemporary antenna structures. The primary reason is the increasing complexity of antenna topologies, a typically large number of adjustable parameters that have to be simultaneously tuned. Design closure is no longer possible using traditional methods, including theoretical models or supervised parameter sweeping. To ensure reliability, optimization is normally carried out at the level of full-wave electromagnetic (EM) simulations, which incurs major computational expenses. The issue can be alleviated using a variety of methods such as the incorporation of adjoint sensitivities, sparse sensitivity updates (for local optimization), or the employment of surrogate modeling methods (in the context of global search). Another possibility is utilization of variable-fidelity simulation models, which, in practice, is most often restricted to two levels (coarse/fine or low-/high-fidelity models), and accompanied by appropriate low-fidelity model correction (e.g., space mapping). This article proposes an accelerated version of a trust-region (TR) gradient-based procedure, which involves simulation model management by continuous adjustment of EM analysis fidelity throughout the optimization process. Decision-making process is based on the convergence status of the algorithm. The initial stages of the optimization run are executed using the coarsest discretization of the structure at hand with the model being gradually refined toward the end of the process. This enables considerable computational savings without degrading the quality of the final design. The presented approach has been comprehensively validated using a benchmark set of four broadband antennas and compared with the reference TR procedure and two state-of-the-art accelerated algorithms. The average computational savings are almost 60% as compared with the reference.