Probabilistic analysis of the convergence of the differential evolution algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24510%2F20%3A00007960" target="_blank" >RIV/46747885:24510/20:00007960 - isvavai.cz</a>
Result on the web
<a href="http://nnw.cz/doi/2020/NNW.2020.30.017.pdf" target="_blank" >http://nnw.cz/doi/2020/NNW.2020.30.017.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2020.30.017" target="_blank" >10.14311/NNW.2020.30.017</a>
Alternative languages
Result language
angličtina
Original language name
Probabilistic analysis of the convergence of the differential evolution algorithm
Original language description
Differential evolution algorithms represent an efficient framework to tackle complicated optimization problems with many variables and involved constraints. Nevertheless, the classic differential evolution algorithms in general do not ensure the convergence to the global minimum of the cost function. Therefore, the authors of the article designed a modification of these algorithms that guarantees the global convergence in the asymptotic and probabilistic sense. The modification consists in adding a certain ratio of random individuals to each generation formed by the algorithm. The random individuals limit the premature convergence to the local minimum and contribute to more thorough exploration of the search space. This article concentrates specifically on the role of random individuals in the identification of the global minimum of the cost function. Besides, the paper also contains some useful estimates of the probability of finding the global minimum of the corresponding cost function.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Netwok World, Volume 30 (2020)
ISSN
2336-4335
e-ISSN
—
Volume of the periodical
30
Issue of the periodical within the volume
4
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
15
Pages from-to
249-263
UT code for WoS article
000589946600003
EID of the result in the Scopus database
2-s2.0-85099372583