Gravitational search algorithm using CUDA: a case study in high-performance metaheuristics

摘要: Many scientific and technical problems with massive computation requirements could benefit from the graphics processing units (GPUs) using compute unified device architecture (CUDA). Gravitational search algorithm (GSA) is a population-based metaheuristic which can be effectively implemented on GPU to reduce execution time. Nonetheless, performance improvement depends strongly process used adapt into CUDA environment. In this paper, we discuss possible approaches parallelize GSA hardware CUDA. An in-depth study of efficiency parallel algorithms capability exploit performed. Additionally, comparative sequential was carried out set standard benchmark optimization functions. The results show significant speedup while maintaining quality re-emphasizes utility CUDA-based implementation for complex computationally intensive applications.