Abstract
Intelligent optimization algorithms such as particle swarm optimization (PSO) have been introduced into four-dimensional variational assimilation of atmospheric data to solve complex optimization problems. The time-varying double compression model can solve the problem of accuracy well. But when confronted with the problem of high accuracy, the long training time will become the weakness. Parallelization acceleration is one of the effective ways to solve the conundrum. And applying Graphic Processing Unit (GPU) to accelerate PSO algorithm in parallel has the advantage of low hardware cost. In this paper, a parallel time-varying double compression factor PSO algorithm based on GPU acceleration is proposed. The parallel operation of particle swarm optimization algorithm is carried out by GPU, in which the time can be improved with the same precision kept. Compared with the dynamic inertia weight algorithm and time-varying double compression factor algorithm, the experimental results display that the accuracy is better than the former and the consuming time is shorter than the latter, which proves that the method can process the prediction in a faster and more accurate way.
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Funding
This research was financially supported by the Application of improved particle swarm optimization in data assimilation (20191050013), Teaching research project of Hubei provincial department of education (2016294, 2017320), humanities and social science research project of Hubei province (17D033).
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Chen, K., Liu, Y., Liu, L., Yu, Y., Dong, Y., Tong, Y. (2020). Research on Atmospheric Data Assimilation Algorithm Based on Parallel Time-Varying Dual Compression Factor Particle Swarm Optimization Algorithm with GPU Acceleration. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_7
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