基于蚁群算法的车辆路径问题研究
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摘要
文中首先对传统的蚁群算法进行了简要介绍,并分析了其局限性;其次详细对比了车辆路径优化与TSP的区别,对于蚁群算法从启发式因子和改进参数两个方面对蚁群算法进行了优化;最后结合某三级城市京东仓库和配送点之间的路径优化进行了实例分析,分析结果表明,基于文中优化后的蚁群算法总里程由原来的465. 6km,缩减为改进后的301. 6km,减少164km,优化效果十分明显。
Firstly,the traditional ant colony algorithm and its limitations is introduced and analyzed. Then it compares the difference between vehicle path optimization and TSP in detail,and the ant colony algorithm is optimized from heuristic factor and improved parameter. Finally,an example is analyzed based on the path optimization between Jingdong warehouse and distribution point in a three-tier city. The analysis results show that the total mileage of the ant colony algorithm based on this paper is reduced164 km from the original 465. 6 km,to the improved 301. 6 km,the optimization effect is obvious.
Firstly,the traditional ant colony algorithm and its limitations is introduced and analyzed. Then it compares the difference between vehicle path optimization and TSP in detail,and the ant colony algorithm is optimized from heuristic factor and improved parameter. Finally,an example is analyzed based on the path optimization between Jingdong warehouse and distribution point in a three-tier city. The analysis results show that the total mileage of the ant colony algorithm based on this paper is reduced164 km from the original 465. 6 km,to the improved 301. 6 km,the optimization effect is obvious.
引文
[1]杨晓芳,姚宇,付强.基于新鲜度的冷链物流配送多目标优化模型[J].计算机应用研究,2016,33(4):1050-1053,1074.
[2]梁海红.“互联网+”时代物流配送中心选址优化模型构建[J].统计与决策,2016(22):51-53.
[3]袁群,左奕.基于改进混合遗传算法的冷链物流配送中心选址优化[J].上海交通大学学报,2016,50(11):1795-1800.
[4]马祥丽,张惠珍,马良.带时间窗物流配送车辆路径问题的蝙蝠算法[J].计算机工程与应用,2016,52(11):254-258,264.
[5]张京敏,牛群.关于城市物流配送交通路径规划仿真研究[J].计算机仿真,2017,34(6):367-371,397.
[6]张倩,鲁渤,杨华龙.物流配送车辆路径问题的鲁棒优化方法[J].系统科学与数学,2017,37(1):79-88.
[7]倪壮,肖刚,敬忠良,等.改进蚁群算法的飞机冲突解脱路径规划方法[J].传感器与微系统,2016,35(4):130-133.
[8]袁亚博,刘羿,吴斌.改进蚁群算法求解最短路径问题[J].计算机工程与应用,2016,52(6):8-12.
[9]Tartakovsky D,Stern E,Broday D M. Dispersion of TSP and PM 10emissions from quarries in complex terrain[J]. Science of the Total Environment,2016,542(Pt A):946-954.
[10]胡宏伟,杜剑,李洋,等.基于稀疏矩阵的两层介质超声相控阵全聚焦成像[J].机械工程学报,2016,53(14):128-135.
[11]Deb S,Fong S,Tian Z,et al. Finding approximate solutions of NPhard optimization and TSP problems using elephant search algorithm[J]. Journal of Supercomputing,2016,72(10):1-33.
[12]Liu M,Zhang F,Ma Y,et al. Evacuation path optimization based on quantum ant colony algorithm[J]. Advanced Engineering Informatics,2016,30(3):259-267.
[13]Zuo L,Lei S,Dong S,et al. A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing[J]. IEEE Access,2017,3:2687-2699.
[14]王松涛.基于优化的遗传算子改进蚁群算法AGV路径规划[J].自动化应用,2017(3):47-49.
[15]Saini R,Anand N. A multi-objective ant colony system algorithm for virtual machine placement[J]. International Journal of Engineering Research&Applications,2017,7(1):95-97.
[2]梁海红.“互联网+”时代物流配送中心选址优化模型构建[J].统计与决策,2016(22):51-53.
[3]袁群,左奕.基于改进混合遗传算法的冷链物流配送中心选址优化[J].上海交通大学学报,2016,50(11):1795-1800.
[4]马祥丽,张惠珍,马良.带时间窗物流配送车辆路径问题的蝙蝠算法[J].计算机工程与应用,2016,52(11):254-258,264.
[5]张京敏,牛群.关于城市物流配送交通路径规划仿真研究[J].计算机仿真,2017,34(6):367-371,397.
[6]张倩,鲁渤,杨华龙.物流配送车辆路径问题的鲁棒优化方法[J].系统科学与数学,2017,37(1):79-88.
[7]倪壮,肖刚,敬忠良,等.改进蚁群算法的飞机冲突解脱路径规划方法[J].传感器与微系统,2016,35(4):130-133.
[8]袁亚博,刘羿,吴斌.改进蚁群算法求解最短路径问题[J].计算机工程与应用,2016,52(6):8-12.
[9]Tartakovsky D,Stern E,Broday D M. Dispersion of TSP and PM 10emissions from quarries in complex terrain[J]. Science of the Total Environment,2016,542(Pt A):946-954.
[10]胡宏伟,杜剑,李洋,等.基于稀疏矩阵的两层介质超声相控阵全聚焦成像[J].机械工程学报,2016,53(14):128-135.
[11]Deb S,Fong S,Tian Z,et al. Finding approximate solutions of NPhard optimization and TSP problems using elephant search algorithm[J]. Journal of Supercomputing,2016,72(10):1-33.
[12]Liu M,Zhang F,Ma Y,et al. Evacuation path optimization based on quantum ant colony algorithm[J]. Advanced Engineering Informatics,2016,30(3):259-267.
[13]Zuo L,Lei S,Dong S,et al. A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing[J]. IEEE Access,2017,3:2687-2699.
[14]王松涛.基于优化的遗传算子改进蚁群算法AGV路径规划[J].自动化应用,2017(3):47-49.
[15]Saini R,Anand N. A multi-objective ant colony system algorithm for virtual machine placement[J]. International Journal of Engineering Research&Applications,2017,7(1):95-97.