Advanced Search
Volume 42 Issue 9
Sep.  2016
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(9): 1785-1794.
ZHANG Zhuhong, ZHANG Renchong. Micro-immune optimization algorithm for solving probabilistic optimization problems[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(9): 1785-1794. doi: 10.13700/j.bh.1001-5965.2015.0563(in Chinese)
Citation: ZHANG Zhuhong, ZHANG Renchong. Micro-immune optimization algorithm for solving probabilistic optimization problems[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(9): 1785-1794. doi: 10.13700/j.bh.1001-5965.2015.0563(in Chinese)
shu

Micro-immune optimization algorithm for solving probabilistic optimization problems

doi: 10.13700/j.bh.1001-5965.2015.0563
  • 1.

    College of Big Data & Information Engineering, Guizhou University, Guiyang 550025, China

  • 2. Institute of System Science and Information Technology, College of Science, Guizhou University, Guiyang 550025, China

Funds:

National Natural Science Foundation of China (61563009); Doctoral Fund of Ministry of Education of China (20125201110003); Graduate Innovation Fund of Guizhou University (2015057) 

  • Received Date: 01 Sep 2015
  • Publish Date: 20 Sep 2016
    Abstract
  • This paper investigates a micro-immune optimization algorithm for the problem of nonlinear probabilistic optimization with unknown random variable distribution. In the design of algorithm, an implicit parallel optimization structure is developed based on the danger theory, while individuals can be identified through a proposed adaptive sampling method. Those danger regions and subpopulations can be decided dynamically through regulating danger radiuses, and meanwhile multiple kinds of mutation strategies are used to guide individuals to move towards multiple directions. Such algorithm has the merits of small population, few adjustable parameters, structural simplicity and so forth; the computational complexity depends on iteration number, variable dimension and population size. Based on the theoretical test examples and a bus scheduling problem, numerically comparative experiments show that the proposed algorithm possesses some advantages of search efficiency and optimized effect, and has potential for solving complex probabilistic optimization problems.

     

    • FullText(HTML)
  • loading
    • References(19)
  • [1]
    LIU B D.Uncertainty theory:A branch of mathematics for modeling human uncertainty [M].Berlin:Springer-Varlag, 2010:81-113.
    [2]
    麻倩倩.一类随机机会约束规划的算法及应用研究[D].保定:华北电力大学,2006:1-28.MA Q Q.Research on the algorithm and application for a class of stochastic chance-constrained programming[D].Baoding:North China Electric Power University,2006:1-28(in Chinese).
    [3]
    LIU B D.Theory and practice of uncertain programming[M].2nd ed.Berlin:Springer-Varlag,2009:25-55.
    [4]
    丁晓东,吴让泉,邵世煌.含有模糊和随机参数的混合机会约束规划模型[J].控制与决策,2002,17(5):587-590.DING X D,WU R Q,SHAO S H.Hybrid programming model with fuzzy and stochastic parameters[J].Control and Decision,2002,17(5):587-590(in Chinese).
    [5]
    NING Y,TANG W,WANG H.Hybrid genetic-SPSA algorithm based on random fuzzy simulation for chance-constrained programming[M]//WANG L,JIN Y.Fuzzy systems and know-ledge discovery.Berlin:Springer-Varlag,2005:332-335.
    [6]
    刘宝碇,赵瑞清.随机规划与模糊规划[M].北京:清华大学出版社,1998:74-94.LIU B D,ZHAO R Q.Stochastic programming and fuzzy programming[M].Beijing:Tsinghua University Press,1998:74-94(in Chinese).
    [7]
    LIU X,LIN L,ZANG D.Stochastic programming models and hybrid intelligent algorithm for unbalanced bidding problem[J].Computer and Information Science,2009,2(1):188-194.
    [8]
    ZHANG H,HA M,XING H.Chance-constrained programming on Sugeno measure space[J].Expert Systems with Applications,2011,38(9):11527-11533.
    [9]
    肖宁,曾建潮.基于随机模拟与 PSO 算法相结合的随机机会约束规划算法[J].计算机应用与软件,2009,26(4):40-41.XIAO N,ZENG J C.Algorithm of stochastic chance-constrained programming based on combination of random simulation and PSO algorithm[J].Computer Application and Software,2009,26(4):40-41(in Chinese).
    [10]
    肖宁.求解随机机会约束规划的混合智能算法[J].计算机工程与应用,2010,46(22):43-46.XIAO N.Solving stochastic chance-constrained programming problems with hybrid intelligent algorithm[J].Computer Engineering and Applications,2010,46(22):43-46(in Chinese).
    [11]
    卢福强,黄敏,王兴伟.虚拟企业风险管理的机会约束规划模型及算法[J].信息与控制,2009,38(4):399-405.LU F Q,HUANG M,WANG X W.Chance-constraint programming model and algorithm for risk management of virtual enterprise[J].Information and Control,2009,38(4):399-405(in Chinese).
    [12]
    艾宁宁.基于混合智能算法求解随机期望值模型和机会约束规划[D].西安:长安大学,2012:1-41.AI N N.A study hybrid algorithm solving stochastic expected value models and chance-constrained programming[D].Xi'an:Chang'an University,2012:1-41(in Chinese).
    [13]
    XIAO N.An algorithm for solving stochastic chance-constrained programming problem[J].Advanced Materials Research,2014,912-914:1138-1141.
    [14]
    段富,杨茸.求解随机机会约束规划的混合智能算法及应用[J].计算机应用,2012,32(8):2230-2234.DUAN F,YANG R.Hybrid intelligent algorithm for solving stochastic chance-constrained programming and its application[J].Journal of Computer Applications,2012,32(8):2230-2234(in Chinese).
    [15]
    MATZINGER P.Tolerance,danger,and the extended family[J].Annual Review of Immunology,1994,12:991-1045.
    [16]
    WU D,GAN D Q,JIANG J N.An improved micro-particle swarm optimization algorithm and its application in transient stability constrained optimal power flow[J].International Transactions on Electrical Energy Systems,2014,24:395-411.
    [17]
    VIVEROS-JIMÉNEZ F，MEZURA-MONTES E，GELBUKH A.Empirical analysis of a micro-evolutionary algorithm for numerical optimization[J].International Journal of Physical Sciences,2012,7(8):1235-1258.
    [18]
    ABIYEV R H,TUNAY M.Optimization of high-dimensional functions through hypercube evaluation[J].Computational Intelligence and Neuroscience,2015,2015:1-11.
    [19]
    许旺土,何世伟,宋瑞,等.多时段公交发车间隔优化的随机期望值模型[J].北京理工大学学报,2009,29(8):676-679.XU W T,HE S W,SONG R,et al.Stochastic expected value model for multiple bus headways optimization[J].Transaction of Beijing Institute of Technology,2009,29(8):676-679(in Chinese).
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(1164) PDF downloads(764) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return