Design Space Reduction for Expedited Multi-Objective Design Optimization of Antennas in Highly Dimensional Spaces

摘要: A surrogate-based technique for efficient multi-objective antenna optimization is discussed. Our approach exploits response surface approximation (RSA) model constructed from low-fidelity data (here, obtained through coarse-discretization electromagnetic simulations). The RSA enables fast determination of the best available trade-offs between conflicting design goals. cost construction multi-parameter antennas significantly lowered initial space reduction. Optimization carried out by a evolutionary algorithm (MOEA). Additional correction techniques are subsequently applied to improve selected designs at level high-fidelity model. refined constitute final Pareto set representation. presented validated using three examples: six-variable ultra-wideband dipole antenna, an eight-variable planar Yagi-Uda and monocone with 13 variables.