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论文类型：期刊论文

发表时间：2009-11-01

发表刊物：INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL

收录刊物：SCIE、EI、Scopus

卷号：5

期号：11A

页面范围：3707-3716

ISSN号：1349-4198

关键字：Ant colony optimization algorithm; Parallel process; CPU; CUDA; Fine-grained

摘要：Fine-grained parallel ant colony optimization algorithm (FGACO), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a FGACO method based on GP U-acceleration, which maps parallel ACO algorithm to GPU through the compute unified device architecture (CUDA). The analytical results demonstrate that, the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGACO solution.