| 摘要: |
| 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。 |
| 关键词: 关键词:车辆路径问题 粒子群算法 动态惯性权重 子群协作 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Vehicle routine problem under the condition of stock shortage and its improved PSO algorithm |
|
FANG Jin2cheng1 , ZHANG Qi2shan2
|
| Abstract: |
| 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. |
| Key words: Key words: vehicle routine p roblem particle swarm algorithm dynamic inertia weight sub2group collaboration |
