摘要
随着互联网技术的快速发展,各行各业所产生的信息数据也在以指数级的速度增长.传统的车辆调度算法已经不能够很好地解决车辆调度问题中出现的实时性,大规模等问题.因此,本文构建了一种基于Hadoop的动态车辆调度并行智能优化算法.该算法以传统遗传算法为基础,通过改善遗传算法全局优化能力弱和收敛于局部次优解的问题,并利用Hadoop平台的并行计算机制对传统遗传算法进行改进,使其能够有效应对大规模、快速响应的车辆调度.数值计算结果表明:基于Hadoop的车辆调度算法能够有效提升传统调度算法的优化性能,在处理大规模车辆调度问题时具有良好的加速比.
With the rapid development of Internet technology, the information data generated by all industries and professions is growing at an exponential rate. The traditional vehicle scheduling algorithm in dealing with dynamic vehicle scheduling problem, already cannot satisfy real-time and large-scale scenario, while big data in Hadoop technology can be a good solution. Therefore, this study constructs a dynamic vehicle scheduling parallel intelligent optimization algorithm based on Hadoop. Based on traditional genetic algorithm, the Hadoop platform parallel computing mechanism is used to improve the weak global optimization ability and converging to local optimal solution of the algorithm. The improved algorithm can effectively cope with massive and rapid response of the vehicle scheduling. The result of numerical calculation shows that the algorithm of vehicle scheduling based on Hadoop can effectively improve the optimization performance of traditional scheduling algorithm and has a good acceleration ratio when dealing with large-scale vehicle scheduling problems.
作者
陈燕
于放
田月
刘璐
CHEN Yan;YU Fang;TIAN Yue;LIU Lu(University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China)
出处
《计算机系统应用》
2018年第10期268-272,共5页
Computer Systems & Applications
关键词
智能调度
HADOOP
车辆调度算法
算法优化
启发式算法
intelligent scheduling
Hadoop
vehicle scheduling algorithm
algorithm to optimize
heuristic algorithm
