Convergence Rate of the Modified Differential Evolution Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F17%3A00005017" target="_blank" >RIV/46747885:24220/17:00005017 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24510/17:00005017

Výsledek na webu

<a href="https://aip.scitation.org/doi/abs/10.1063/1.5013964" target="_blank" >https://aip.scitation.org/doi/abs/10.1063/1.5013964</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.5013964" target="_blank" >10.1063/1.5013964</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Convergence Rate of the Modified Differential Evolution Algorithm

Popis výsledku v původním jazyce

Differential evolution algorithms represent an efficient framework to solve complicated optimization tasks with many variables and complex constraints. Nevertheless, the classic differential evolution algorithm does not guarantee the convergence to the global minimum of the cost function. Therefore, the authors developed a modification of this algorithm that ensures asymptotic global convergence. The article provides a comparison of the ability to identify the global minimum of the cost function for the following three algorithms: the classic differential evolution algorithm, the above mentioned modified differential evolution algorithm and an algorithm of random sampling enhanced by a hill climbing procedure. We designed a series of numerical experiments to perform this comparison. The results indicate that the classic differential evolution algorithm is in general an extremely poor global optimizer (global minimum found in 2% of cases). On the other hand the performance of the modified differential evolution algorithm was considerably better (global minimum found in 83% of cases).

Název v anglickém jazyce

Convergence Rate of the Modified Differential Evolution Algorithm

Popis výsledku anglicky

Differential evolution algorithms represent an efficient framework to solve complicated optimization tasks with many variables and complex constraints. Nevertheless, the classic differential evolution algorithm does not guarantee the convergence to the global minimum of the cost function. Therefore, the authors developed a modification of this algorithm that ensures asymptotic global convergence. The article provides a comparison of the ability to identify the global minimum of the cost function for the following three algorithms: the classic differential evolution algorithm, the above mentioned modified differential evolution algorithm and an algorithm of random sampling enhanced by a hill climbing procedure. We designed a series of numerical experiments to perform this comparison. The results indicate that the classic differential evolution algorithm is in general an extremely poor global optimizer (global minimum found in 2% of cases). On the other hand the performance of the modified differential evolution algorithm was considerably better (global minimum found in 83% of cases).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

AIP Conference Proceedings 1910, 030005 (2017)

ISBN

978-0-7354-1602-4

ISSN

0094-243X

e-ISSN

—

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

AMER INST PHYSICS, 2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA

Místo vydání

USA

Místo konání akce

Sozopol, BULGARIA

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000423866900027