A Differential Evolution Algorithm in the Optimization Task with a Lipschitz Continuous Cost Function

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24510%2F19%3A00006394" target="_blank" >RIV/46747885:24510/19:00006394 - isvavai.cz</a>
Result on the web
<a href="https://aip.scitation.org/doi/abs/10.1063/1.5082064" target="_blank" >https://aip.scitation.org/doi/abs/10.1063/1.5082064</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1063/1.5082064" target="_blank" >10.1063/1.5082064</a>

Result language
angličtina
Original language name
A Differential Evolution Algorithm in the Optimization Task with a Lipschitz Continuous Cost Function
Original language description
Differential evolution algorithms represent nowadays an efficient framework to cope with complex optimization tasks with many variables and involved constraints. Nevertheless, the classic differential evolution algorithms do not ensure the global convergence to the minimum of the cost function. That is why the author designed a modification of these algorithms that guarantees asymptotic global convergence in the probabilistic sense. The contribution investigates some relevant properties of the modified differential evolution algorithms under the assumption that the cost function of the optimization task is Lipschitz continuous. In such a case it is possible to deduce some relations between the number of generations used in the algorithm and the probability to achieve the global minimum of the cost function.
Czech name
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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