Abstract
Recently, more and more researchers apply intelligent algorithms to solve conditional nonlinear optimal perturbation (CNOP) which is proposed to study the predictability of numerical weather and climate prediction. The difficulty of solving CNOP using intelligent algorithm is the high dimensionality of complex numerical models. Therefore, previous researches either are just tested in ideal models or have low time efficiency in complex numerical models which limited the application of CNOP. This paper proposes a sensitive area selection-based particle swarm optimization algorithm (SASPSO) for fast solving CNOP. Meanwhile, we adopt the self-adaptive dynamic control swarm size strategy to SASPSO method and parallel SASPSO with MPI. To demonstrate the validity, we take Zebiak-Cane (ZC) numerical model as a case. Experimental results show that the proposed method can obtain a better CNOP more efficiently than SAEP [1] and PCAGA [2] which are two latest researches on intelligent algorithms for solving CNOP.
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References
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 41405097).
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Yuan, S., Ji, F., Yan, J., Mu, B. (2015). A Parallel Sensitive Area Selection-Based Particle Swarm Optimization Algorithm for Fast Solving CNOP. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_9
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