Abstract
With the development of Graphics Processing Unit (GPU) and the Compute Unified Device Architecture (CUDA) platform, researchers shift their attentions to general-purpose computing applications with GPU. In this paper, we present a novel parallel approach to run artificial fish swarm algorithm (AFSA) on GPU. Experiments are conducted by running AFSA both on GPU and CPU respectively to optimize four benchmark test functions. With the same optimization performance, the running speed of the AFSA based on GPU (GPU-AFSA) can be as 30 time fast as that of the AFSA based on CPU (CPU-AFSA). As far as we know, this is the first implementation of AFSA on GPU.
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