Variational Data Assimilation Method Using Parallel Dual Populations Particle Swarm Optimization Algorithm

WU Zhongjian ¹LI Junyan²

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作者信息

1. Detroit Green Institute of Technology,Hubei University of Technology,Wuhan 430068,Hubei,China
2. School of Information Management,Central China Normal University,Wuhan 430079,Hubei,China
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Abstract

In recent years,numerical weather forecasting has been increasingly emphasized.Variational data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optimization challenge.The enormity of the dataset under consideration gives rise to substantial computational burdens,complex modeling,and high hardware requirements.This paper em-ploys the Dual-Population Particle Swarm Optimization(DPSO)algorithm in variational data assimilation to enhance assimilation accu-racy.By harnessing parallel computing principles,the paper introduces the Parallel Dual-Population Particle Swarm Optimization(PDPSO)Algorithm to reduce the algorithm processing time.Simulations were carried out using partial differential equations,and compari-sons in terms of time and accuracy were made against DPSO,the Dynamic Weight Particle Swarm Algorithm(PSOCIWAC),and the Time-Varying Double Compression Factor Particle Swarm Algorithm(PSOTVCF).Experimental results indicate that the proposed PDPSO out-performs PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO.Regarding processing time,PDPSO is 40％faster than PSOCIWAC and PSOTVCF and 70％faster than DPSO.

Key words

parallel algorithm/variational data assimilation/dual-population particle swarm optimization algorithm/diffusion mechanism

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基金项目

Hubei Provincial Department of Education Teaching Research Project(2016294)

Hubei Provincial Department of Education Teaching Research Project(2017320)

Hubei Provincial Humanities and Social Science Research Project(17D033)

College Students Innovation and Entrepreneurship Training Program(National)(20191050013)

Hubei Province Natural Science Foundation General Project(2021CFB584)

2023 College Student Innovation and Entrepreneurship Training Program Project(202310500047)

2023 College Student Innovation and Entrepreneurship Training Program Project(202310500049)

出版年

2024

武汉大学自然科学学报(英文版)

武汉大学

武汉大学自然科学学报(英文版)

CSTPCDCSCD

影响因子：0.066

ISSN：1007-1202

参考文献量18

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