Abstract
Acoustic metasurfaces play a key role in building acoustics, noise control, and acoustic cloaking by regulating the acoustic wave scattering characteristics through subwavelength structures. The design of diffusely reflecting metasurfaces aims to achieve a uniform distribution of a scattered field, which is essentially a high-dimensional nonconvex optimization problem that needs to balance the computational efficiency in the synergistic optimization of the spatial arrangement of cells and the angular response. In traditional methods, a heuristic algorithm is prone to local optimization, and it is difficult to balance the global search and local adjustment. And full-wave simulation is time consuming and seriously restricts the design efficiency. Therefore, the hybrid tornado-gradient descent optimization algorithm (VDGD) is proposed in this paper. It uses a two-stage collaborative optimization approach to refine the reflection angle distribution of acoustic metasurfaces, thereby enhancing the uniformity of the diffuse acoustic field. The Tornado Optimization Algorithm (TOA) was initially employed to introduce global perturbations to the randomly initialized design. Local optimization can be avoided by gradually decreasing the perturbation magnitude, which reduces the standard deviation of the sound field from about 5.81 dB to about 4.07 dB. Then, the gradient descent is used for local fine adjustment to further reduce the standard deviation to about 1.91 dB. Experimental results show that the VDGD algorithm outperforms the seven classical and up-to-date optimization algorithms in improving scattering uniformity. This method achieves an effective balance between global search and local fine tuning, providing an efficient and flexible optimization strategy for metasurface design, which can bring application support for intelligent acoustic devices and sound field regulation.