文章摘要
齐名军,吴凯.改进粒子群算法在农产品物流配送路径管理中的应用[J].包装工程,2019,40(17):110-115.
QI Ming-jun,WU Kai.Application of Improved Particle Swarm Optimization Algorithm in Agricultural Product Logistics Distribution Path Management[J].Packaging Engineering,2019,40(17):110-115.
改进粒子群算法在农产品物流配送路径管理中的应用
Application of Improved Particle Swarm Optimization Algorithm in Agricultural Product Logistics Distribution Path Management
投稿时间:2018-10-07  修订日期:2019-09-10
DOI:10.19554/j.cnki.1001-3563.2019.17.016
中文关键词: 粒子群  物流配送  猴群跳跃  权重系数
英文关键词: particle swarm  logistics distribution  monkey group jumping  weight coefficient
基金项目:国家自然科学基金(50138110)
作者单位
齐名军 1.鹤壁职业技术学院河南 鹤壁 458030 
吴凯 2.天津农学院天津 300191 
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中文摘要:
      目的 为了更加合理地进行车辆路径调度管理,提高粒子群求解车辆路径优化问题的性能。方法 提出了一种动态猴子跳跃机制的粒子群优化算法,它借助群体的动态分组,采用不同的动态惯性权重来提高算法的速度,引入猴子跳跃机制来保证全局收敛性。最后把改进算法应用到物流配送路径优化的2个实例中,同一环境下,改进算法搜寻到最优路径适应值、平均运算时间,以及求得最优解的成功次数,均优于标准粒子群优化算法。结果 结果表明,改进的算法能快速有效地确定物流配送路径。结论 改进粒子群优化算法不仅具有较快的寻优速度,而且也提高了算法的收敛性,保证了寻优质量,因此具有很大的应用价值。
英文摘要:
      The work aims to more rationally carry out vehicle routing management, and improve the performance of particle swarm optimization to solve the problem of vehicle routing optimization. A particle swarm optimization algorithm based on dynamic monkey jumping mechanism was proposed. By means of the dynamic grouping of groups, different dynamic inertia weights were used to improve the speed of the algorithm. Monkey jumping mechanism was introduced to ensure global convergence. Finally, the improved algorithm was applied to two examples of logistics distribution path optimization. Under the same environment, the number of successful cases that the improved algorithm found the optimal path adaptation value and the average operation time and obtained the optimal solution was better than the standard particle swarm optimization algorithm. The results showed that, the improved algorithm could quickly and efficiently determine the logistics distribution path. The improved particle swarm optimization algorithm not only has faster speed of optimization, but also improves the convergence of the algorithm and ensures the optimization quality; therefore, it has great application value.
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