An efficient parameter-retrieval-based surrogate-assisted optimization of on-platform honeycomb absorbing structures

Abstract An electromagnetic parameter-retrieval-based surrogate-assisted optimization (PSAO) algorithm is presented to reduce radar cross section (RCS) by optimizing the on-platform honeycomb absorbing structures. To facilitate the optimization process, the honeycomb structure is transformed to an anisotropic homogeneous slab, and the effective parameters of the slab are extracted by the retrieval algorithm. A multi-fidelity model is employed to reduce the computing-time consumption, in which a Gaussian process (GP) regression model is used as the substitute for the coarse model. The GP model establishes a relationship between the geometry of the honeycomb structure and the RCS response of the target coated with the equivalent slab. Finally, the optimization result of the fine model is achieved through a space mapping strategy. The accuracy of the parameter extraction algorithm is verified by analyzing the honeycomb absorbing structure. Subsequently, the proposed optimization method is applied to a metal plate and a metal cylinder, resulting in a 10 dB reduction of RCS in broadband and wide-angle scenarios. This demonstrates the applicability of the proposed PSAO algorithm to both planar and conformal on-platform honeycomb absorbing structures. Furthermore, an NACA0015 foil is analyzed, showing an average RCS reduction of 10 dB and a minimum RCS reduction of 5 dB in the X-band. These results indicate that the PSAO approach can effectively apply to complicated targets. Additionally, the proposed method exhibits significant advantages in terms of computational accuracy and efficiency compared to the traditional genetic algorithm.