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Title:
Paralelni algoritem diferencialne evolucije in gručenja za multimodalno optimizacijo : diplomsko delo
Authors:
ID
Ravnak, Tilen
(
Author
)
ID
Bošković, Borko
(
Mentor
)
More about this mentor...
ID
Brest, Janez
(
Comentor
)
Files:
UN_Ravnak_Tilen_2021.pdf
(1,23 MB)
MD5: CA11FD33E0E63051B812D65860200E45
PID:
20.500.12556/dkum/6e0b4e7f-5e9d-4564-84d5-2d92ecbadacf
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu predstavljamo implementacijo paralelne različice algoritma diferencialne evolucije in gručenja za multimodalno optimizacijo. Algoritem smo paralelizirali tako na centralni procesni enoti kot na grafični procesni enoti. Za paralelizacijo na centralni procesni enoti smo uporabili knjižnico thread (niti), ki je del programskega jezika C++. Za paralelizacijo na grafični procesni enoti smo uporabili ogrodje CUDA. Uspešnost algoritma smo testirali na izbranih optimizacijskih problemih tekmovanja CEC 2013. Najvišji faktor pohitritve smo dosegli pri problemu Modified Rastrigin in sicer 2,799.
Keywords:
Diferencialna evolucija
,
gručenje
,
paralelizacija
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[T. Ravnak]
Year of publishing:
2021
Number of pages:
IX, 36 str.
PID:
20.500.12556/DKUM-80462
UDC:
004.8.021(043.2)
COBISS.SI-ID:
96310787
Publication date in DKUM:
18.10.2021
Views:
983
Downloads:
59
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
14.09.2021
Secondary language
Language:
English
Title:
A parallel differential evolution clustering algorithm for multimodal optimization
Abstract:
In this diploma thesis we present the implementation of a parallel version of the differential evolution and clustering for multimodal optimization algorithm. The algorithm was paralleled on the central as well as the graphics processing unit. Parallelization on the central processing unit was done using the thread library, which is part of the C++ programming language. For parallelization on the graphics processing unit, we used the CUDA framework. We evaluated the performance of the algorithm based on select optimization problems presented at the CEC 2013 competition. We achieved the highest speedup of 2,799 at the Modified Rastrigin problem.
Keywords:
Differetnial evolution
,
clustering
,
parallelization
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