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The robust constant and its applications in random global search for unconstrained global optimization

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Abstract

Robust analysis is important for designing and analyzing algorithms for global optimization. In this paper, we introduce a new concept, robust constant, to quantitatively characterize the robustness of measurable sets and functions. The new concept is consistent to the theoretical robustness presented in literatures. This paper shows that, from the respects of convergence theory and numerical computational cost, robust constant is valuable significantly for analyzing random global search methods for unconstrained global optimization.

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Notes

  1. The robustness of a function defined by Zheng [32] will be introduced in the next section.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Zheng Peng

  2. Department of Mathematics, College of Science, Shanghai University, Shanghai, 200444, China

    Donghua Wu

  3. Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, 350108, China

    Wenxing Zhu

Authors
  1. Zheng Peng
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  2. Donghua Wu
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  3. Wenxing Zhu
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Corresponding author

Correspondence to Zheng Peng.

Additional information

This work was supported by the Natural Science Foundation of China (61170308), Major Science Foundation of Fujian Provincial Department of Education (JA14037) and talent foundation of Fuzhou University (XRC-1043).

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Peng, Z., Wu, D. & Zhu, W. The robust constant and its applications in random global search for unconstrained global optimization. J Glob Optim 64, 469–482 (2016). https://doi.org/10.1007/s10898-014-0256-1

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  • DOI: https://doi.org/10.1007/s10898-014-0256-1

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