Abstract
PurposeA novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.Design/methodology/approachThe keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.FindingsThe presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.Research limitations/implicationsThe proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.Originality/valueThe proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.
Highlights
Increasing performance requirements have made the design of contemporary antenna structures a challenging endeavor
This paper presents a novel knowledge-based methodology for low-cost and reliable optimization of antenna structures
Notes: The initial design lies on sx(F) and its corresponding performance figure vector Ftmp.0 does not coincide with the target vector Ft because sx(F) ≠ O(F)
Summary
Increasing performance requirements have made the design of contemporary antenna structures a challenging endeavor. The originality and technical contributions of this work include the development of an automated and reliable machine learning optimization framework using pre-existing knowledge on the antenna structure at hand, development of iterative correction procedure for rapid design refinement, demonstration of a possibility of expedited design while handling various performance figures and operating conditions (e.g. operating frequencies, realized gain, substrate permittivity), demonstrated capability of quasi-global design closure using primarily local methods. One of its distinctive features is the incorporation of available information about the antenna of interest in the form of previously obtained designs, either available from the previous work with the structure or prepared to set up the framework This information is blended into a kriging surrogate model which yields – for a given target vector of performance figures – a reasonably good initial design (Section 2.2). Having defined the objective space and the merit function, the design optimality can be formulated as a solution to the following nonlinear minimization problem: x* 1⁄4 arg min UðRðxÞ; F Þ (2)
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