A Parallel Surrogate-Assisted Multi-Penalty Function Search for Simulation-Driven Antenna Design

Jin Wang, Qingbin Guo, Haoran Gu, Handing Wang

Abstract: High-fidelity electromagnetic simulation-driven optimization are crucial in modern antenna design. However, many antenna optimization models involve multiple expensive constraints, which can be formulated as expensive constrained optimization problems (ECOPs). Currently, surrogate-assisted evolutionary algorithms are widely used to solve ECOPs. However, existing methods face significant challenges in addressing errors in constraint surrogate models and the strong conflicts among constraints and the objective, making it difficult to find feasible solutions with optimal objective value within a limited number of simulations. We propose a parallel surrogate-assisted multiple penalties search method for simulation-driven antenna design problems with expensive conflicting constrains. In the proposed method, a multi-penalty function search mechanism is designed, followed by an adaptive parallel sampling approach to collect multiple samples for the parallel simulation. The experimental results applied to the design of the three-layer filtering antenna demonstrate the effectiveness and great potential of the proposed method in addressing simulation-driven antenna design problems with expensive conflicting constrains.