彭勇

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:水利工程系

学科:水文学及水资源

办公地点:大连理工大学水利工程学院综合3#实验楼436

联系方式:电话:0411-84707911

电子邮箱:pengyong@dlut.edu.cn

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Multi-Core Parallel Particle Swarm Optimization for the Operation of Inter-Basin Water Transfer-Supply Systems

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论文类型:期刊论文

发表时间:2017-01-01

发表刊物:WATER RESOURCES MANAGEMENT

收录刊物:SCIE、EI、Scopus

卷号:31

期号:1

页面范围:27-41

ISSN号:0920-4741

关键字:Inter-basin water transfer-supply systems; Multi-core; Parallel particle swarm optimization; Joint operation model; Fork/join framework

摘要:To optimize the joint operation model for inter-basin water transfer-supply systems (IBWTS), this study proposes three multi-core parallel particle swarm optimization (PPSO) algorithms (i.e., PPSO_ring, PPSO_star, PPSO_share). These algorithms are based on the Fork/Join framework and the concurrency in Java. The biggest difference between the proposed PPSOs and the traditional one, which is also based on the Fork/Join framework, is that the former can exchange information among the threads (sub-swarms), while the latter cannot. To implement the proposed PPSOs, the Fork/Join framework is used to assign threads to different CPU cores, thereby evolving the standard PSO separately, and the synchronization-and-communication mechanisms of concurrency in Java is used to exchange information among the threads. The North-line IBWTS in Liaoning Province of China is taken as a case study to test the proposed algorithms. The analysis of the algorithms demonstrate that all the three proposed PPSOs outperform the traditional one, which indicates that information exchange among the sub-swarms can improve algorithm performance. PPSO_share performs better than PPSO_ring and PPSO_star, which illustrates that when each sub-swarm's best particle, the particle's best position and the best particle of the whole swarm are used to update the particle's velocity, the algorithm performance can be further improved. The operation results show that PPSO_share can take full advantage of multi-core resources and enhance the computing efficiency and solution accuracy of the joint operation model, showing its potential practicability and validity for complex multi-reservoir system operations in the future.