摘要
物流配送路径优化是一类实用价值很高的NP完全难题,针对传统启发式优化算法搜索速度慢、易陷入局部最优解的缺点,提出了一种量子蚁群算法的物流配送路径优化方法(QACA)。在物流配送路径优化问题分析的基础上建立相应的数学模型,通过量子蚁群算法对其进行求解,对各路径上的信息素进行量子比特编码,采用量子旋转门及最优路径对信息素进行更新,对QACA的性能进行仿真测试。仿真结果表明,QACA具有较强的全局搜索能力和收敛速度,可以有效解决物流配送路径问题。
The logistics distribution route problem is a NP problem which possesses important practical value. A novel optimization method of logistics distribution route is proposed based on Quantum Ant Colony Algorithm （QACA） to overcome the problems such as long computing time and easy to fall into local best for traditional heuristic optimization algorithm. The optimization problem of logistics distribution routing is analyzed, and the mathematical model is established, and then the quantum ant colony algorithm is used to solve it, and the pheromone on each path is encoded by a group of quantum bits, and a new pheromone rep- resentation is designed, called quantum pheromone, and the quantum rotation gate and the best tour are applied to updating the pheromone. The simulation test is carried out to test the performance of QACA. The simulation results show that, QACA has a strong global search ability and convergence speed, and can effectively solve the logistics distribution routing problem.
出处
《计算机工程与应用》
CSCD
2013年第21期56-59,共4页
Computer Engineering and Applications
基金
湖南省教育厅科学研究项目(No.08D093)
关键词
物流配送
路径选择
量子计算
蚁群算法
转移概率
physical distribution
routing selection
quantum computing
ant colony algorithm
transition probability
