计算机与现代化

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基于Spark和PSO算法的军事物流配送路径优化问题研究

  

  1. (1.华北计算技术研究所系统一部,北京100083;2.军事科学院系统工程院网络信息所,北京100141)
  • 收稿日期:2018-05-18 出版日期:2018-11-22 发布日期:2018-11-23
  • 作者简介:张利娟(1989-),女,山东滕州人,华北计算技术研究所系统一部硕士研究生,研究方向:大数据处理,数据挖掘; 仇建伟(1960-),男,江苏南京人,研究员级高级工程师,硕士,研究方向:系统体系结构,图形图像,指挥控制系统; 杜登崇(1980-),男,军事科学院系统工程院网络信息所工程师,博士,研究方向:计算机技术应用; 王鑫(1982-),男,高级工程师,硕士,研究方向:综合电子信息系统,指挥信息系统,军事大数据系统,军事业务信息系统。

Research on Military Logistics Distribution Routing Optimization Problem #br# Based on Spark and PSO Algorithm

  1. (1. System No.1, North China Institute of Computing Technology, Beijing 100083, China;
    2. Network Information Institute of Systems Engineering, Academy of Military Sciences, Beijing 100141, China)
  • Received:2018-05-18 Online:2018-11-22 Published:2018-11-23

摘要: 军事物流配送路径优化问题是研究如何在保证各个部队所需物资的前提下,各配送车辆总行驶路径最短的问题。利用粒子群优化(Particle Swarm Optimization, PSO)算法解决该类问题时,随着部队数量的增加,程序运行时间会显著增加。考虑到PSO算法迭代计算的特点,本文提出一种在Spark集群上并行运行PSO算法的解决方案。实验证明,利用Spark集群并行运行PSO算法能够大幅降低程序运行时间,提高解决军事物流配送路径优化问题的效率。

关键词: 军事物流配送路径优化问题, 粒子群优化算法, Spark

Abstract: Research on the military logistics distribution routing optimization is to study how to guarantee the shortest route of the vehicles under the premise of ensuring the supply of the troops. Using Particle Swarm Optimization (PSO) algorithm to solve this problem, the program running time will increase significantly with the increase of troop numbers. Considering the characteristics of algorithm iteration calculation, a solution to parallel running PSO algorithm on Spark cluster is proposed. Experimental results show that the parallel running PSO algorithm using Spark cluster can greatly reduce the program running time and improve the efficiency of military logistics distribution routing optimization problem.

Key words: military logistics distribution routing optimization problem, particle swarm optimization algorithm, Spark

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