An Efficient Fine-Grained Parallel Particle Swarm Optimization Method Based On Gpu-Acceleration

Fine-grained parallel particle swarm optimization (FGPSO), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a FGPSO method based on GPU-acceleration, which maps a parallel PSO algorithm to texture-rendering on consumer-level graphics cards. The analytical results demonstrate that the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGPSO solution.