Surrogate-Assisted Evolutionary Multi-Objective Antenna Design

This paper presents a multi-problem surrogate-assisted evolutionary multi-objective optimization approach for antenna design. By transforming the traditional antenna design optimization problem into expensive multi-objective optimization problems, this method employs a multi-problem surrogate (MPS) model to stack multiple antenna design problems. The MPS model is a knowledge-transfer framework that stacks multiple surrogate models (e.g., Gaussian Processes) trained on related antenna design problems (e.g., Yagi–Uda antennas with varying director configurations) to accelerate optimization. The parameters of Yagi–Uda antenna including radiation patterns and beamwidth—across various director configurations are considered as decision variables. The several surrogates are constructed based on the number of directors of Yagi–Uda antenna. The MPS algorithm identifies promising candidate solutions using an expected improvement strategy and refines them through true function evaluations, effectively balancing exploration with computational cost. Compared to benchmark algorithms assessed by hypervolume, our approach demonstrated superior average performance while requiring fewer function evaluations.