A parallel particle swarm optimization algorithm based on GPU/CUDA

Parallel computing is the main way to improve the computational efficiency of metaheuristic algorithms for solving high-dimensional, nonlinear optimization problems. Previous studies have typically only implemented local parallelism for the particle swarm optimization (PSO) algorithm. In this study, we proposed a new parallel particle swarm optimization algorithm (GPU-PSO) based on the Graphics Processing Units (GPU) and Compute Unified Device Architecture (CUDA), which uses a combination of coarse-grained parallelism and fine-grained parallelism to achieve global parallelism. In addition, we designed a data structure based on CUDA features and utilized a merged memory access mode to further improve data-parallel processing and data access efficiency. Experimental results show that the algorithm effectively reduces the solution time of PSO for solving high-dimensional, large-scale optimization problems. The speedup ratio increases with the dimensionality of the objective function, where the speedup ratio is up to 2000 times for the high-dimensional Ackley function.