Volume 41 Issue 2
Feb.  2016
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WU Hui, LIU Yongbo, QIN Chengzhi, LIU Junzhi, JIANG Jingchao, ZHU A-Xing. Parallelization of an Optimization Algorithm for Beneficial Watershed Management Practices[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 202-207. DOI: 10.13203/j.whugis20140048
Citation: WU Hui, LIU Yongbo, QIN Chengzhi, LIU Junzhi, JIANG Jingchao, ZHU A-Xing. Parallelization of an Optimization Algorithm for Beneficial Watershed Management Practices[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 202-207. DOI: 10.13203/j.whugis20140048
shu

Parallelization of an Optimization Algorithm for Beneficial Watershed Management Practices

  • 1.

    Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China;

  • 3.

    Department of Geography, University of Guelph, Guelph, Ontario N1G 2W1, Canada;

  • 4.

    Nanjing Normal University, Nanjing 210097, China;

  • 5.

    Department of Geography, University of Wisconsin-Madison, Madison WI 53706, USA

Funds: The National High Technology Research and Development Program of China (863 Program), No. 2011AA120305; the National Science and Technology Support Program, No. 2013BAC08B03-4; the Major Science and Technology Program for Water Pollution Control and Treatment, No. 2013ZX07103006-005.
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  • Received Date: February 19, 2014
  • Published Date: February 04, 2016
    Graphical Abstract
    • Abstract
  • The optimization of beneficial management practices (or beneficial management practices, BMPs) is a typical case of complex geo-computation; a computation-intensive search for optimal solutions of watershed BMPs through many iterative watershed model simulations. This paper presents a parallelization of the epsilon non-dominated sorted genetic algorithm (ε-NSGA-Ⅱ), an increasingly widely-used algorithm for BMPs optimization. The proposed parallel optimization algorithm was designed based on a master-slave parallelization strategy and implemented using the message passing interface (MPI). A case study executed on an IBM cluster for the Meichuan Jiang watershed (about 6366 km2) in the Lake Poyang basin shows that the proposed parallel BMPs optimization algorithm performs well. When the count of cores used in the case study increased (8~512 cores), the proposed parallel optimization algorithm delivered a higher speedup ratio. The speedup ratio reached 310 when 512 cores were used. In this case study, the parallel efficiency of the proposed parallel BMPs optimization algorithm decreased with an increase of the count of cores. The parallel efficiency ranged from 0.61 to 0.91, demonstrating that the proposed algorithm achieves good parallel performance.
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    • References (15)
  • [1]
    Logan T J. Agricultural Best Management Practices for Water-Pollution Control-Current issues[J]. Agriculture Ecosystems & Environment, 1993, 46(1-4):223-321
    [2]
    Ripa M, Leone A, Garnier M, et al. Agricultural Land Use and Best Management Practices to Control nonpoint Water Pollution[J]. Environmental Management, 2006, 38(2):253-266
    [3]
    Rao N S, Easton Z M, Schneiderman E M, et al. Modeling Watershed-scale Effectiveness of Agricultural Best Management Practices to Reduce Phosphorus Loading[J]. Journal of Environmental Management, 2009, 90(3):1385-1395
    [4]
    Arabi M, Govindaraju R S, Hantush M M. Cost-effective Allocation of Watershed Management Practices Using a Genetic Algorithm[J]. Water Resources Research, 2006, 42(10):W10429-1-14
    [5]
    Gitau M W, Chiang L C, Sayeed M, et al. Watershed Modeling Using Large-Scale Distributed Computing in Condor and the Soil and Water Assessment Tool model[J]. SIMULATION, 2012, 88(3):365-380
    [6]
    Rouholahnejad E, Abbaspour K C, Vejdani M, et al. A Parallelization Framework for Calibration of Hydrological Models[J]. Environmental Modelling & Software, 2012, 31(5):28-36
    [7]
    Li Jian, Li Deren, Shao Zhenfeng. A Streaming Data Delaunay Triangulation Algorithm Based on Parallel Computing[J]. Geomatics and Information Science of Wuhan University, 2013, 38(7):794-798(李坚, 李德仁, 邵振峰. 一种并行计算的流数据Delaunay构网算法[J]. 武汉大学学报·信息科学版, 2013, 38(7):794-798)
    [8]
    Qi Kunlun, Chen Yumin, Wu Huayi, et al. Parallel processing in Eigenfunction-based Spatial Filtering Using MPI+OpenMP Hybrid Parallelization[J]. Geomatics and Information Science of Wuhan University, 2013, 38(6):742-750(祁昆仑, 陈玉敏, 吴华意, 等. MPI+OpenMP环境下的特征函数空间滤值并行化方法研究[J]. 武汉大学学报·信息科学版, 2013, 38(6):742-750)
    [9]
    Maringanti C, Chaubey I. High Performance Computing Application to Address Non-point Source Pollution at a Watershed Level[C]. American Society of Agricultural and Biological Engineers Annual International Meeting. Reno, NV, 2009
    [10]
    Kollat J B, Reed P M. The Value of Online Adaptive Search:A Performance Comparison of NSGAⅡ, ε-NSGAⅡ and εMOEA[C]. Third International Conference, EMO 2005, Guanajuato, Mexico, 2005
    [11]
    Kollat J B, Reed P M. Comparing State-of-the-Art Evolutionary Multi-objective Algorithms for Long-term Groundwater Monitoring Design[J]. Advances in Water Resources, 2006, 29(6):792-807
    [12]
    Devireddy V, Reed P. Efficient and Reliable Evolutionary Multiobjective Optimization Using ε-dominance Archiving and Adaptive Population Sizing[C]. Genetic and Evolutionary Computation Genetic and Evolutionary Computation Conference, Seattle, WA, USA, 2004
    [13]
    Gropp W, Lusk E, Doss N, et al. A High-performance, Portable Implementation of the MPI Message Passing Interface Standard[J]. Parallel Computing, 1996, 22(6):789-828
    [14]
    Liu J, Zhu A, Liu Y, et al. A Layered Approach to Parallel Computing for Spatially Distributed Hydrological Modeling[J]. Environmental Modelling & Software, 2014, 51(1):221-227
    [15]
    Chaubey I, Maringanti C, Popp J. Development of a Multiobjective Optimization Tool for the Selection and Placement of Best Management Practices for Nonpoint Source Pollution Control[J]. Water Resources Research, 2009, 45(6):W06406-1-15
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