A surrogate-based method for efficient multi-objective antenna optimization is presented. Our technique exploits response surface approximation (RSA) model constructed from sampled low-fidelity antenna model (here, obtained through coarse-discretization EM simulation). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. A low-cost RSA model construction is possible through initial reduction of the design space. Optimization of the RSA model has been carried out using a multi-objective evolutionary algorithm (MOEA). Additional response correction techniques have been subsequently applied to improve selected designs at the high-fidelity EM antenna model level. The refined designs constitute the final Pareto set representation. The proposed approach has been validated using an ultra-wideband (UWB) monocone and a planar Yagi-Uda antenna.
Autorzy
Informacje dodatkowe
- DOI
- Cyfrowy identyfikator dokumentu elektronicznego link otwiera się w nowej karcie 10.1016/j.procs.2014.05.071
- Kategoria
- Publikacja w czasopiśmie
- Typ
- artykuły w czasopismach
- Język
- angielski
- Rok wydania
- 2014
Źródło danych: MOSTWiedzy.pl - publikacja "Low-Cost EM-Simulation-Driven Multi-Objective Optimization of Antennas" link otwiera się w nowej karcie