摘要
为提高最短路径求解问题的效率,提出一种基于云计算的细粒度混合并行遗传算法求解最短路径的方法。方法采用云计算中H adoop的Map Reduce并行编程模型,提高编码效率,同时将细粒度并行遗传算法和禁忌搜索算法结合,提高了寻优算法的计算速度和局部寻优能力,进而提高最短路径的求解效率。仿真结果表明,该方法在计算速度和性能上优于经典遗传算法和并行遗传算法,是一种有效的最短路径求解方法。
The shortest path problem plays an important role in many application research areas. In order to improve the efficiency of solving shortest path problem, a cloud computing-based hybrid parallel genetic algorithm was proposed in this paper. The MapReduce parallel programming model of Hadoop distributed computing platform in cloud computing was used to improve the coding efficiency. Based on the combination of fine grained parallel genetic algorithm and tabu search algorithm, the computing speed and search ability of the optimal algorithm were also improved, and thus the efficiency of solving shortest path problem was enhanced. The simulation results show that the computation speed and performance of the proposed algorithm were better than classical genetic algorithm and parallel genetic algorithm, and presented method is an effective shortest path solving method.
出处
《电子技术应用》
北大核心
2015年第3期123-125,129,共4页
Application of Electronic Technique
关键词
云计算
遗传算法
禁忌搜索算法
最短路径
cloud computing
genetic algorithm
tabu search algorithm
shortest path
