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

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dc.contributor.author Knobloch, Roman
dc.date.accessioned 2019-08-16T09:13:20Z
dc.date.available 2019-08-16T09:13:20Z
dc.date.issued 2018
dc.identifier.uri https://dspace.tul.cz/handle/15240/153220
dc.identifier.uri https://aip.scitation.org/doi/pdf/10.1063/1.5082064?class=pdf
dc.description.abstract 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 article shows that Lipschitz continuity of the cost function is a reasonable assumption making possible quantitative considerations and estimates. The next part describes how the cost function domain is explored by random individuals. More random individuals mean more detailed sampling of the cost function domain. This fact is the base for the asymptotic convergence of the modified differential evolution algorithm. cs
dc.format.extent 8 stran cs
dc.language.iso cs cs
dc.relation.ispartof PROCEEDINGS OF THE 44TH INTERNATIONAL CONFERENCE "APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS"
dc.title A Differential Evolution Algorithm in the Optimization Task with a Lipschitz Continuous Cost Function cs
dc.type Proceedings Paper
dc.identifier.doi 10.1063/1.5082064
local.relation.volume 2048
local.event.sdate 2018-06-06
local.event.sdate 2018-06-13
local.event.title 44th International Conference on Applications of Mathematics in Engineering and Economics
local.event.location Sozopol, BULGARIA
local.identifier.publikace 6394
local.article.number 30006


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