A Framework for Multiobjective Antenna Optimization Based on Differential Evolution of Deep Gaussian Processes: DGP–DEMO

In recent years, antenna design problems have become increasingly high-dimensional, strongly nonlinear, and tightly constrained. Existing surrogate-assisted frameworks for antenna optimization are limited in representational capacity, calibrated uncertainty, and handling of multiobjective trade-offs; consequently, they struggle to balance global exploration with precise convergence under restricted simulation budgets. To address these limitations, we propose DGP–DEMO, a surrogate-assisted multiobjective optimization framework for antenna design. DGP–DEMO employs a deep Gaussian process (DGP) as an online surrogate that continuously learns the mapping from design variables to electromagnetic responses and yields per-sample, calibrated uncertainty estimates; couples heuristic search with a Pareto-based fitness to efficiently generate and screen candidates across the parameter space; and introduces a surrogate-guided local refinement stage to improve feasibility and convergence quality. Case studies on two complex antennas show that, relative to existing surrogate-assisted frameworks, DGP–DEMO achieves effective multiobjective optimization of electromagnetic performance with fewer simulations and exhibits strong practical applicability.