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.
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Wong, T.H., Qin, A.K., Wang, S., Shi, Y. (2015). cuSaDE: A CUDA-Based Parallel Self-adaptive Differential Evolution Algorithm. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_30
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DOI: https://doi.org/10.1007/978-3-319-13356-0_30
Publisher Name: Springer, Cham
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