A surrogate-based technique for efficient multi-objective antenna optimization is discussed. Our approach exploits response surface approximation (RSA) model constructed from low-fidelity antenna model data (here, obtained through coarse-discretization electromagnetic simulations). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. The cost of RSA model construction for multi-parameter antennas is significantly lowered through initial design space reduction. Optimization of the response surface approximation model is carried out by a multi-objective evolutionary algorithm. Additional response correction techniques are subsequently applied to improve selected designs at the level of high-fidelity electromagnetic antenna model. The refined designs constitute the final Pareto set representation. The pre-sented multi-objective design approach is validated using three examples: a six-variable ultra-wideband dipole antenna, an eight-variable planar Yagi-Uda anten-na and an ultra-wideband monocone with thirteen design variables.
Authors
Additional information
- Category
- Publikacja monograficzna
- Type
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language
- angielski
- Publication year
- 2014
