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Sample bound estimate based chance-constrained immune optimization and its applications

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Abstract

This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.

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Author information

Authors and Affiliations

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

    Zhu-Hong Zhang & Da-Min Zhang

  2. College of Computer Science & Technology, Guizhou University, Guizhou, 550025, China

    Kai Yang

Authors
  1. Zhu-Hong Zhang
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  2. Kai Yang
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  3. Da-Min Zhang
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Corresponding author

Correspondence to Zhu-Hong Zhang.

Additional information

This work was supported in part by National Natural Science Foundation NSFC (Nos. 61563009 and 61065010) and Doctoral Fund of Ministry of Education of China (No. 20125201110003).

Zhu-Hong Zhang received his M. Sc. degree from Department of Mathematics, Guizhou University, China in 1998 and Ph.D. degree from College of Automation, Chongqing University, China in 2004. He is a professor at Guizhou University, China. Prof. Zhang has been an associate editor affiliated with Journal of Applied Soft Computing since 2010. He has published about 80 refereed journal and conference papers and also one book on modern intelligent algorithms (the 2nd author).

His research interests include evolutionary computation, immune optimization, uncertain programming, control theory, and visual neural networks.

ORCID iD: 0000-0001-7619-1040

Kai Yang received his B. Sc. and M. Sc. degrees from Departments of Mechanical Engineering and Computer Science, Guizhou University, China in 1998 and 2008, respectively. He is currently a Ph.D. degree candidate at College of Computer Science, Guizhou University, China.

His research interests include immune optimization and stochastic programming.

Da-Min Zhang received his M. Sc. and Ph.D. degrees from Guizhou University, China in 2005 and 2010, respectively. He, as a professor at Guizhou University, has published about 30 refereed journal and conference papers.

His research interest include computer system integration, software development and complex networks.

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Zhang, ZH., Yang, K. & Zhang, DM. Sample bound estimate based chance-constrained immune optimization and its applications. Int. J. Autom. Comput. 13, 468–479 (2016). https://doi.org/10.1007/s11633-016-0997-z

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  • DOI: https://doi.org/10.1007/s11633-016-0997-z

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