摘要
物流中的车辆路径问题(VRP)是目前组合优化领域的研究热点问题,VRP为NP-hard问题。本文在对VRP分析的基础上,建立数学模型,提出了一种适合求解该问题的蚁群遗传融合优化算法。提出的优化算法首先采用蚁群算法在局部阶段产生最好解,然后利用遗传算法的优良基因在全局阶段对优化解进一步优化,以获取最好路径解。实验结果表明,提出的融合算法能高效解决VRP问题,且优化效果比单算法好。
The logistics distribution VRP,which is a typical NP-hard problem,is a hot topic in the combinatorial optimization field at present.Based on the analysis about VRP,a mathematical model is built.Aiming at solving the vehicle routing problem,the paper puts forward a combinatorial optimization algorithm of ant colony and genetics in order to gain optimization.The combinatorial optimization algorithm adopts the ant colony algorithm to gain local optimization solution,and then makes use of the genetic algorithm which reserves some elitist genetic sense units that can steadily pass down to the son generation to optimize the local optimization solution for gaining a global optimization solution.The experimental results show that the combination optimization algorithm is efficient in solving VRP,and the optimization efficiency of the improved algorithm is superior to that of a single algorithm such as the ant colony algorithm or the genetic algorithm.
出处
《计算机工程与科学》
CSCD
北大核心
2011年第5期106-111,共6页
Computer Engineering & Science
基金
江苏省高校自然科学基础研究(09KJB120003)
徐州师范大学项目基金(08XBL14)
关键词
车辆路径问题
融合优化算法
蚁群算法
遗传算法
路径优化
vehicle routing problem
combination optimization algorithm
ant colony algorithm
genetic algorithm
route optimization
