CUDA-Based Particle Swarm Optimization in Reflectarray Antenna Synthesis

The synthesis of electrically large, highly performing reflectarray antennas can be computationally very demanding both from the analysis and from the optimization points of view. It therefore requires the combined usage of numerical and hardware strategies to control the computational complexity and provide the needed acceleration. Recently, we have set up a multi-stage approach in which the first stage employs global optimization with a rough, computationally convenient modeling of the radiation, while the subsequent stages employ local optimization on gradually refined radiation models. The purpose of this paper is to show how reflectarray antenna synthesis can take profit from parallel computing on Graphics Processing Units (GPUs) using the CUDA language. In particular, parallel computing is adopted along two lines. First, the presented approach accelerates a Particle Swarm Optimization procedure exploited for the first stage. Second, it accelerates the computation of the field radiated by the reflectarray using a GPU-implemented Non-Uniform FFT routine which is used by all the stages. The numerical results show how the first stage of the optimization process is crucial to achieve, at an acceptable computational cost, a good starting point.