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The robust constant and its applications in random global search for unconstrained global optimization.
- Source :
- Journal of Global Optimization; Mar2016, Vol. 64 Issue 3, p469-482, 14p
- Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09255001
- Volume :
- 64
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Journal of Global Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 113084317
- Full Text :
- https://doi.org/10.1007/s10898-014-0256-1