Abstract
Parametric optimization is a mandatory step in the design of contemporary antenna structures. Conceptual development can only provide rough initial designs that have to be further tuned, often extensively. Given the topological complexity of modern antennas, the design closure necessarily involves full-wave electromagnetic (EM) simulations and—in many cases—global search procedures. Both factors make antenna optimization a computationally expensive endeavor: population-based metaheuristics, routinely used in this context, entail significant computational overhead. This letter proposes a novel approach that interleaves trust-region gradient search with iterative parameter space exploration by means of local kriging surrogate models. Dictated by efficiency, the latter are rendered in low-dimensional subspaces spanned by the principal components of the antenna response Jacobian matrix, extracted to identify the directions of the maximum (frequency-averaged) response variability. The aforementioned combination of techniques enables quasi-global search at the cost comparable to local optimization. These features are demonstrated using two antenna examples as well as benchmarking against multiple-start local tuning.
Highlights
Development of modern antennas necessarily involves parameter tuning, typically being the last stage of the design process. It is executed at the level of full-wave electromagnetic (EM) simulation models
Examples include pattern synthesis of antenna arrays [5], The associate editor coordinating the review of this manuscript and approving it for publication was Mohammed Bait-Suwailam
Other available techniques include multiple-start local optimization, the Taguchi method involving experimental design by orthogonal arrays [20], as well as combinations of metaheuristics with gradient-based procedures (e.g., [21]) or variable-fidelity simulations (e.g., [22]), both incorporated to speed up the convergence process
Summary
Development of modern antennas necessarily involves parameter tuning, typically being the last stage of the design process. Other available techniques include multiple-start local optimization, the Taguchi method involving experimental design by orthogonal arrays [20], as well as combinations of metaheuristics with gradient-based procedures (e.g., [21]) or variable-fidelity simulations (e.g., [22]), both incorporated to speed up the convergence process. Interleaving the trust-region algorithm and surrogate-assisted exploration results in quasi-global search capabilities while maintaining computational efficiency of the process This is comprehensively demonstrated using two antenna examples.
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