Computationally-efficient multi-objective optimization of antenna structures using point-by-point Pareto set identification and local approximation surrogates

The paper presents a computationally-efficient methodology for multi-objective optimization of antenna structures. In our approach, the set of designs representing the best possible trade-offs between conflicting objectives is obtained by moving along the Pareto front and identifying the subsequent Pareto-optimal solutions using surrogate-based optimization techniques. For the sake of computational efficiency we also utilize coarse-discretization electromagnetic (EM) simulations and local response surface approximation models. The proposed approach is demonstrated using a ultrawideband dipole antenna with the 9-element representation of the Pareto front obtained at the total cost corresponding to only 30 evaluations of the high-fidelity EM antenna model.