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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

AIP Conference Proceedings

ISBN

978-073541774-8

ISSN

0094243X

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

American Institute of Physics

Místo vydání

—

Místo konání akce

44th International Conference on Applications of

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—