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GPU-based parallel particle swarm optimization
Abstract
A novel parallel approach to run standard particle swarm optimization (SPSO) on Graphic Processing Unit (GPU) is presented in this paper. By using the general-purpose computing ability of GPU and based on the software platform of Compute Unified Device Architecture (CUDA) from NVIDIA, SPSO can be executed in parallel on GPU. Experiments are conducted by running SPSO both on GPU and CPU, respectively, to optimize four benchmark test functions. The running time of the SPSO based on GPU (GPU-SPSO) is greatly shortened compared to that of the SPSO on CPU (CPU-SPSO). Running speed of GPU-SPSO can be more than 11 times as fast as that of CPU-SPSO, with the same performance, compared to CPU-SPSO, GPU-SPSO shows special speed advantages on large swarm population applications and high dimensional problems, which can be widely used in real optimizing problems.
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Sengupta, S., Basak, S., & Peters, R. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010010 · Cited by 543
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Gong, Y.-J., Chen, W.-N., Zhan, Z.-H., Zhang, J., Li, Y., Zhang, Q., & Li, J.-J. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.04.061 · Cited by 393
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Metadata
- Authors
- Zhou, Y., & Tan, Y.
- Venue
- 2009 IEEE Congress on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2009-05
- Pages
- 1493-1500
- OpenAlex
- W2040919914
- Cited by
- 199
- References
- 0
- FWCI
- 11.28
- Type
- conference paper
- Language
- en
- Open access
- closed
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