基于GPU加速遗传算法的直接定位研究

he genetic direct position determination (DPD) algorithm executes slowly and has poor real-time performance under the large-scale data condition. This paper proposed a GPU-based genetic DPD algorithm to overcome the above shortcoming. According to the cost function of DPD, it designed a high speed parallel architecture of GPU. It reduced the execution time and improved efficiency, according to the parallel design of the fitness function and the genetic operation such as selection, crossover and mutation. The experiments show that, through reasonable design of the parallel thread architecture of GPU, the proposed method can reduce the execution time of the genetic DPD algorithm efficiently and locate the emitter faster.