cuSaDE: A CUDA-based parallel self-adaptive differential evolution algorithm

Wong, T, Qin, K, Wang, S and Yuhui, S 2014, 'cuSaDE: A CUDA-based parallel self-adaptive differential evolution algorithm', in Hisashi Handa, Hisao Ishibuchi, Yew-Soon Ong, Kay Chen Tan (ed.) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014) - Volume 2, Singapore, 10-12 November 2014, pp. 375-388.


Document type: Conference Paper
Collection: Conference Papers

Title cuSaDE: A CUDA-based parallel self-adaptive differential evolution algorithm
Author(s) Wong, T
Qin, K
Wang, S
Yuhui, S
Year 2014
Conference name IES 2014
Conference location Singapore
Conference dates 10-12 November 2014
Proceedings title Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014) - Volume 2
Editor(s) Hisashi Handa, Hisao Ishibuchi, Yew-Soon Ong, Kay Chen Tan
Publisher Springer
Place of publication Switzerland
Start page 375
End page 388
Total pages 14
Abstract Differential evolution (DE) is a powerful population-based stochastic optimization algorithm, which has demonstrated high efficacy in various scientific and engineering applications. Among numerous variants of DE, self-adaptive differential evolution (SaDE) features the automatic adaption of the employed search strategy and its accompanying parameters via online learning the preceding behavior of the already applied strategies and their associated parameter settings. As such, SaDE facilitates the practical use of DE by avoiding the considerable efforts of identifying the most effective search strategy and its associated parameters. The original SaDE is a CPU-based sequential algorithm. However, the major algorithmic modules of SaDE are very suitable for parallelization. Given the fact that modern GPUs have become widely affordable while enabling personal computers to carry out massively parallel computing tasks, this work investigates a GPU-based implementation of parallel SaDE using NVIDIA's CUDA technology. We aim to accelerate SaDE's computation speed while maintaining its optimization accuracy. Experimental results on several numerical optimization problems demonstrate the remarkable speedups of the proposed parallel SaDE over the original sequential SaDE across varying problem dimensions and algorithmic population sizes.
Subjects Neural, Evolutionary and Fuzzy Computation
DOI - identifier 10.1007/978-3-319-13356-0_30
Copyright notice © Springer International Publishing Switzerland 2015
ISBN 9783319133553
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