[1]马青宇,邵松帅,刘博旭,等.基于改进麻雀搜索算法的冷链物流路径优化[J].计算机技术与发展,2024,34(03):125-132.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 019]
　MA Qing-yu,SHAO Song-shuai,LIU Bo-xu,et al.Optimization of Cold Chain Logistics Path Based on Improved Sparrow Search Algorithm[J].,2024,34(03):125-132.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 019]
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基于改进麻雀搜索算法的冷链物流路径优化()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年03期
页码:
125-132
栏目:
人工智能
出版日期:
2024-03-10

文章信息/Info

Title:
Optimization of Cold Chain Logistics Path Based on Improved Sparrow Search Algorithm
文章编号:
1673-629X(2024)03-0125-08
作者:
马青宇1; 2 ; 邵松帅1; 2 ; 刘博旭1; 2 ; 孙 哲1; 2 ; 龚光富3 ; 孙知信1; 2*
1. 南京邮电大学 江苏省邮政大数据技术与应用工程研究中心,江苏 南京 210023;
2. 南京邮电大学 国家邮政局邮政行业技术研发中心(物联网技术),江苏 南京 210023;
3. 安徽邮谷快递智能科技有限公司,安徽 芜湖 241300
Author(s):
MA Qing-yu1; 2 ; SHAO Song-shuai1; 2 ; LIU Bo-xu1; 2 ; SUN Zhe1; 2 ; GONG Guang-fu3 ; SUN Zhi-xin1; 2 *
1. Post Big Data Technology and Application Engineering Research Center of Jiangsu Province,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;
2. Post Industry Technology Research and Development Center of the State Posts Bureau ( Internet of Things Technology) ,Nanjing University of Posts and Telecommunications,　Nanjing 210023,China;
3. Anhui Yougu Express Intelligent Technology Co. ,Ltd. ,Wuhu 241300,China
关键词:
冷链物流; 麻雀搜索算法; 离散化; 邻域学习; 动态因子
Keywords:
cold chain logistics; sparrow search algorithm; discretization; neighborhood learning; dynamic factor
分类号:
TP39;U492. 3+36. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 03. 019
摘要:
针对城市间冷链物流对成本与时效的高要求,提出了一种改进的离散麻雀搜索算法。 通过对麻雀维度序列的映射,实现了算法的离散化;引入基于维度的邻域模型以加强麻雀种群内的信息交流,降低陷入局部最优解的可能;引入动态因子以改进发现者位置更新公式,平衡算法的开发与勘探。 采用 23 个标准测试函数进行测试,实验所得平均值与方差表明,改进算法的搜索性能与稳定性得到了极大的改善。 采用 6 个标准 VRPTW 数据集测试改进算法求解复杂路径优化问题的能力,对比实验表明,改进算法能够以更快的速度求得更优的可行解,验证了改进算法的有效性与稳定性。 最后使用小规模数据集可视化展示了改进算法在路径规划问题的提升。
Abstract:
Aiming at the high requirements of inter-city cold chain logistics in terms of cost and time efficiency,an improved discretesparrow search algorithm is proposed. The discrete algorithm is realized by mapping the sparrow dimensional sequences;introducing a dimension-based neighborhood model to enhance the information exchange within the sparrow population and reduce the possibility offalling into local optimal solutions; introducing dynamic factors to improve the discoverer position update formula and balance thedevelopment and exploration of the algorithm. Twenty - three standard test functions were used for testing,and the mean and varianceobtained from the experiments showed that the search performance and stability of the improved algorithm were greatly improved. Sixstandard VRPTW datasets were used to test the ability of the improved algorithm?
to solve complex path optimization problems. The comparison experiments show that the improved algorithm can find better feasible solutions at a faster rate,which verifies the effectiveness andstability of the improved algorithm. Finally,the enhancement of the improved algorithm for the path planning problem is demonstratedvisually using a small-scale dataset.

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更新日期/Last Update: 2024-03-10