库存不足条件下车辆路径问题及其改进PSO算法 3
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Vehicle routine problem under the condition of stock shortage and its improved PSO algorithm
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    摘 要:分析并构建了库存不足条件下车辆路径问题的数学模型;在模型的求解上,提出一种基于子群 协作的动态粒子群算法;最后通过算例实验表明:该算法能有效克服标准粒子群算法迭代寻优时选择步长 的盲目性,也改善了算法求解时容易陷入局部最优、导致早熟的缺陷,具有较强的全局寻优能力,收敛速度 快,计算精度高。

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    Abstract: This paper analyzed and established mathematic models for vehicle routine p roblem under the con2 dition of stock shortage. To solve the models, it p resented a dynamic particle swarm op timization algorithm based on sub2group collaboration. Finally, the paper made some experimental calculations, and the results of calculations p roved that the algorithm could avoid blind search effectively, and overcome the limitation of easily trapp ing in local extreme points and leading to p remature, as a result, it had better capability of global op timization, higher speed of convergence and p recision than standard particle swarm op timization.

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方金城 , 张岐山.库存不足条件下车辆路径问题及其改进PSO算法 3[J].重庆工商大学学报(自然科学版),2009,(6):553-557
FANG Jincheng, ZHANG Qishan. Vehicle routine problem under the condition of stock shortage and its improved PSO algorithm[J]. Journal of Chongqing Technology and Business University(Natural Science Edition),2009,(6):553-557

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