摘要
针对大规模高维数复杂非线性函数优化的问题,提出一种新的基于GPU的协同差分进化算法。该方法将协同进化的思想引入启发式差分进化算法,随机分解大规模计算问题,利用GPU处理数据的并行性,同步计算分解后的子问题,加快算法的精度和收敛速度。实验对比结果表明,所提出的基于GPU的协同差分进化算法对大规模非线性函数优化具有更高的精度和效率。
In order to solve large-scale high-dimensional nonlinear function optimization problem, a novel cooperative differential evolution algorithm based on GPU is proposed. The coevolution principle is introduced to the heuristic differential evolution algorithm; and then the large-scale computational problems are decomposited randomly. Using GPU parallelism, simultaneous calculate decomposed sub-problems, which improves the accuracy and speeds up the algorithm convergence process. Experimental comparison results show that the proposed GPU-based cooperative differential evolution algorithm has better accuracy and efficiency for large-scale nonlinear function optimizations.
出处
《计算机工程与应用》
CSCD
2012年第7期48-50,123,共4页
Computer Engineering and Applications
关键词
并行计算
协同进化
差分进化
图形图像处理单元
parallel calculation
cooperation evolution
differential evolution
Graphic Processing Uni（tGPU）
