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Publikační činnost
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stať ve sborníku (D)
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Katedra informatiky a počítačů (31400)
Title:
Parallel Migration Models Applied to Competitive Differential Evolution
Citace
BUJOK, P. a Tvrdík, J. Parallel Migration Models Applied to Competitive Differential Evolution.
In:
13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.
Los Alamitos: IEEE Computer Society, 2011. IEEE Computer Society, 2011. s. 306-313. ISBN 978-0-7695-4630-8.
Subtitle
Publication year:
2011
Obor:
Informatika
Number of pages:
8
Page from:
306
Page to:
313
Form of publication:
ISBN code:
978-0-7695-4630-8
ISSN code:
Proceedings title:
13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Proceedings:
Mezinárodní
Publisher name:
IEEE Computer Society
Place of publishing:
Los Alamitos
Country of Publication:
Sborník vydaný v zahraničí
Název konference:
13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Conference venue:
Timisoara, Romania
Datum zahájení konference:
Typ akce podle státní
příslušnosti účastníků:
Celosvětová akce
WoS code:
EID:
Key words in English:
Differential evolution parallel models algorithm performance experimental comparison
Annotation in original language:
The influence of parallelism on the performance of competitive adaptive differential evolution is studied. Two serial competitive differential evolution variants described in literature and sixteen novel parallel variants were experimentally compared. All the parallel differential evolution variants in this study are based on a migration model with the star topology. The algorithms were compared on six benchmark functions with two levels of dimension (D = 10 and D = 30). The number of the function evaluations and the reliability rate of the search were used as basic characteristics of algorithm?s performance. The experimental results show that the parallelism applied to competitive differential evolution together with a proper setting of the parameters controlling the parallel model can improve the performance of the algorithm and decrease the computational costs significantly at least in some problems.
Annotation in english language:
References
Reference
R01:
RIV/61988987:17310/11:A12013DZ
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