Study on cascade reservoirs optimal operation based on parallel normal cloud mutation shuffled frog leaping algorithm

被引:0
|
作者
Wang, Li-Ping [1 ]
Sun, Ping [1 ]
Jiang, Zhi-Qiang [1 ]
Zhang, Yan-Ke [1 ]
Zhang, Pu [1 ]
机构
[1] Renewable Energy College, North China Electric Power University, Beijing,102206, China
关键词
Application programs - Software testing;
D O I
暂无
中图分类号
学科分类号
摘要
To improve the premature convergence problem of traditional shuffled frog leaping algorithm (SFLA), in this paper, cloud model algorithm mix together with SFLA algorithm, then a normal cloud mutation shuffled frog leaping algorithm (normal cloud mutation SFLA, NCM-SFLA) is proposed, which is to make up the shortage of shuffled frog leaping algorithm that is easy to fall into local optimal solution. At the same time, the algorithm is easy to be parallel, parallel extensions are used to parallel optimization of algorithm in multi core environment. Then the algorithms are applied to cascade reservoirs optimal operation. The test case of practical application shows that, compared with the multidimensional dynamic programming algorithm (MDP), NCM-SFLA has better global search ability and fast convergence speed, and the parallel algorithm can effectively shorten the running time of program in the calculation of existing conditions. Moreover, to solve the cascade reservoirs optimal operation is reasonable, effective by using the new algorithms. ©, 2015, Systems Engineering Society of China. All right reserved.
引用
收藏
页码:790 / 798
相关论文
共 50 条
  • [41] Unsupervised segmentation of images based on Shuffled Frog-Leaping Algorithm
    Tehami A.
    Fizazi H.
    Tehami, Amel (tehami@.amel), 1600, Korea Information Processing Society (13): : 370 - 384
  • [42] A Web Document Classification Method Based on Shuffled Frog Leaping Algorithm
    Sun, Xia
    Wang, Ziqiang
    Zhang, Dexian
    SECOND INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING: WGEC 2008, PROCEEDINGS, 2008, : 205 - 208
  • [43] Regular Expression Grouping Optimization Based on Shuffled Frog Leaping Algorithm
    Cai Liangwei
    Yi Haoping
    2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2016, : 1111 - 1115
  • [44] Distributed wind generator planning based on shuffled frog leaping algorithm
    Zhang, Shenxi
    Chen, Kai
    Long, Yu
    Cheng, Haozhong
    Li, Ke
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2013, 37 (13): : 76 - 82
  • [45] Opposition based learning ingrained shuffled frog-leaping algorithm
    Sharma, Tarun Kumar
    Pant, Millie
    JOURNAL OF COMPUTATIONAL SCIENCE, 2017, 21 : 307 - 315
  • [46] Multiobjective Optimization for Optimal Placement and Size of DG using Shuffled Frog Leaping Algorithm
    Yammani, Chandrasekhar
    Maheswarapu, Sydulu
    Matam, Sailajakumari
    2011 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY ENGINEERING (ICAEE), 2012, 14 : 990 - 995
  • [47] Multi-class LSTMSVM based on optimal directed acyclic graph and shuffled frog leaping algorithm
    Zhang, Xiekai
    Ding, Shifei
    Sun, Tongfeng
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2016, 7 (02) : 241 - 251
  • [48] Adaptive Shuffled Frog Leaping Algorithm For Optimal Power Rate Allocation: Power Line
    Altrad, Abdullah. M. M.
    Amphwan, Angela
    Hilles, Shadi M. S.
    2018 INTERNATIONAL CONFERENCE ON SMART COMPUTING AND ELECTRONIC ENTERPRISE (ICSCEE), 2018,
  • [49] Robot path planning based on shuffled frog leaping algorithm combined with genetic algorithm
    Zhang, Zhaojun
    Sun, Rui
    Xu, Tao
    Lu, Jiawei
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 44 (03) : 5217 - 5229
  • [50] Solving the Time Optimal Traveling Salesman Problem Based on Hybrid Shuffled Frog Leaping Algorithm-genetic Algorithm
    Zhang Yong
    Gao Xinxin
    Wang Yujie
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2018, 40 (02) : 363 - 370