Improving convergence properties of a differential evolution algorithm

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F16%3A00000845" target="_blank" >RIV/46747885:24220/16:00000845 - isvavai.cz</a>
Alternative codes found
RIV/46747885:24510/16:00000845
Result on the web
<a href="http://dx.doi.org/10.1063/1.4968451" target="_blank" >http://dx.doi.org/10.1063/1.4968451</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1063/1.4968451" target="_blank" >10.1063/1.4968451</a>

Result language
angličtina
Original language name
Improving convergence properties of a differential evolution algorithm
Original language description
The differential evolution is a popular and efficient way to solve complicated optimization tasks with manyvariables and constraints. In this article we study the ability of the differential evolution algorithms to attain the globalminimum of the cost function. We demonstrate that although often declared as a global optimizer the classic differentialevolution algorithm does not guarantee the convergence to the global minimum. To improve this weakness we design amodification of the classic differential evolution algorithm to enrich the diversity of its populations. This modificationlimits the premature convergence to local minima and ensures the asymptotic global convergence. We tested the modifiedalgorithm in numerical experiments and compared the efficiency in finding the global minimum for the classic andmodified algorithm. The modified algorithm is significantly more efficient with respect to the global convergence thanthe classic algorithm.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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