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

Zheng Peng (), Donghua Wu () and Wenxing Zhu ()

Journal of Global Optimization, 2016, vol. 64, issue 3, 469-482

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. Copyright Springer Science+Business Media New York 2016

Keywords: Unconstrained global optimization; Robust constant; Random global search; Pure adaptive search; Algorithm analysis (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10898-014-0256-1

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