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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
CUDA
Based Enhanced Differential Evolution: A Computational Analysis |
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Authors: |
Donald Davendra, Jan Gaura,
Magdalena Bialic-Davendra, Roman Senkerik |
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Published in:
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(2012).ECMS
2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann. European
Council for Modeling and Simulation. doi:10.7148/2012 ISBN:
978-0-9564944-4-3 26th
European Conference on Modelling and Simulation, Shaping reality through simulation Koblenz,
Germany, May 29 – June 1 2012 |
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Citation
format: |
Davendra, D., Gaura,
J., Bialic-Davendra, M., & Senkerik,
R. (2012). CUDA Based Enhanced Differential Evolution: A Computational
Analysis. ECMS 2012 Proceedings edited by: K. G. Troitzsch,
M. Moehring, U. Lotzmann (pp. 399-404). European Council for
Modeling and Simulation. doi:10.7148/2012-0399-0404 |
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DOI: |
http://dx.doi.org/10.7148/2012-0399-0404 |
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Abstract: |
General purpose graphic programming unit (GPGPU) programming is a novel
approach for solving parallel variable independent problems. The graphic
processor core (GPU) gives the possibility to use multiple blocks, each of
which contains hundreds of threads. Each of these threads can be visualized
as a core onto itself, and tasks can be simultaneously sent to all the threads
for parallel evaluations. This research explores the advantages of applying a evolutionary algorithm (EA) on the GPU in terms of
computational speedups. Enhanced Differential Evolution (EDE) is applied to
the generic permutative flowshop
scheduling (PFSS) problem both using the central processing unit (CPU) and
the GPU, and the results in terms of execution time is compared. |
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