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This paper studies parallelizing CLPSO by open computing language (OpenCL) on the integrated Intel HD Graphics 520 (IHDG520) graphical processing unit (GPU) with a low clock rate. We implement a coarse-grained all-GPU model that maps each particle to a separate work item. Two enhancement strategies, namely, generating and transferring random numbers from the central processor to the GPU as well as reducing the number of instructions in the kernel, are proposed to shorten the model\u2019s execution time. This paper further investigates parallelizing deterministic optimization for implicit stochastic optimization of China\u2019s Xiaowan Reservoir. The deterministic optimization is performed on an ensemble of 62 years\u2019 historical inflow records with monthly time steps, is solved by CLPSO, and is parallelized by a coarse-grained multipopulation model extended from the all-GPU model. The multipopulation model involves a large number of work items. Because of the capacity limit for a buffer transferring data from the central processor to the GPU and the size of the global memory region, the random number generation strategy is modified by generating a small number of random numbers that can be flexibly exploited by the large number of work items. Experiments conducted on various benchmark functions and the case study demonstrate that our proposed all-GPU and multipopulation parallelization models are appropriate; and the multipopulation model achieves the consumption of significantly less execution time than the corresponding sequential model.<\/jats:p>","DOI":"10.1155\/2020\/6589658","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T23:32:18Z","timestamp":1596238338000},"page":"1-17","source":"Crossref","is-referenced-by-count":1,"title":["Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing Unit"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5591-3526","authenticated-orcid":true,"given":"Xiang","family":"Yu","sequence":"first","affiliation":[{"name":"Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of 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