Abstract
To date, space mapping remains one of the most efficient design optimisation methodologies in microwave engineering. Still, its performance depends on the underlying surrogate model, in particular, its approximation and generalisation capabilities. By proper selection of the space mapping transformations and their parameters, a trade-off between these can be obtained. Often, this is done by trial and error that may lead to excessive computational overhead and poor quality of the optimisation outcome. In this study, an adaptively constrained parameter extraction is introduced. Based on convergence results for space mapping algorithms, it allows us to automatically find the approximation-generalisation trade-off through the adjustment of the surrogate model parameter space. Improved performance of the space mapping algorithm is obtained both in terms of convergence properties and the quality of the optimised design. Algorithm convergence is additionally improved by constraining the surrogate optimisation process. The authors' technique is validated using several microwave design problems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.