D
Dmitri E. Kvasov
Researcher at University of Calabria
Publications - 48
Citations - 1921
Dmitri E. Kvasov is an academic researcher from University of Calabria. The author has contributed to research in topics: Global optimization & Lipschitz continuity. The author has an hindex of 24, co-authored 46 publications receiving 1607 citations. Previous affiliations of Dmitri E. Kvasov include N. I. Lobachevsky State University of Nizhny Novgorod & Sapienza University of Rome.
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Algorithm 829: Software for generation of classes of test functions with known local and global minima for global optimization
TL;DR: In this paper, a procedure for generating non-differentiable, continuously differentiable, and twice continuous differentiable classes of test functions for multiextremal multidimensional box-constrained global optimization is presented.
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Software for Generation of Classes of Test Functions with Known Local and Global Minima for Global Optimization
TL;DR: In this article, a procedure for generating non-differentiable, continuously differentiable, and twice continuous differentiable test functions for multiextremal multidimensional box-constrained global optimization and a corresponding package of C subroutines are presented.
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Global Search Based on Efficient Diagonal Partitions and a Set of Lipschitz Constants
TL;DR: In this paper, the global optimization problem of a multidimensional "black-box" function satisfying the Lipschitz condition over a hyperinterval with an unknown Lipchitz constant is considered.
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On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget.
TL;DR: Results of more than 800,000 runs on 800 randomly generated tests show that both stochastic nature-inspired metaheuristics and deterministic global optimization methods are competitive and surpass one another in dependence on the available budget of function evaluations.
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Globally-biased Disimpl algorithm for expensive global optimization
TL;DR: A globally-biased simplicial partition Disimpl algorithm for global optimization of expensive Lipschitz continuous functions with an unknown LipsChitz constant is proposed and a scheme for an adaptive balancing of local and global information during the search is introduced.