基于k均值聚类和人工鱼群算法的车辆路径优化研究

De-gang Ji, Dong-mei Huang
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引用次数: 4

摘要

车辆路径问题是物流系统中的一个重要问题。由于其NP-hard性质,当约束较多时,很难得到最优解。针对物流配送车辆路径优化问题,提出了一种基于k均值聚类和人工鱼群算法的车辆路径优化复合算法。结果表明,该算法可以减少算法的输入,提高收敛速度。计算结果表明,复合算法对VRP的求解结果具有一定的竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A research based on K-means clustering and Artificial Fish-Swarm Algorithm for the Vehicle Routing Optimization
Vehicle Routing Problem(VRP) is an important problem in logistic system. Because of its NP-hard property, it is difficult to get the optimal solution when the constrains are more. Aiming at the problem of logistics distribution vehicle routing optimization, this paper provide a composite algorithm based on the K-means clustering and the Artificial Fish-Swarm Algorithm for the vehicle routing optimization(KMAFA). The results indicate that the algorithm can reduce the input of the algorithm and improve the converging speed. The computational result shows that the results of composite algorithm for VRP are competitive.
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