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Volume 42 Issue 9
Sep.  2016
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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.

     

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