摘要
在分析研究车辆路径问题的基础上,将其转换为经典TSP优化问题进行求解并建立数学模型,针对遗传算法在求解车辆路径问题时搜索效率低,容易陷入局部最优的缺点,提出了一种改进的遗传算法.改进算法引用自适应邻域法进行种群初始化;基于捕食搜索策略动态自适应调整遗传参数,在加快寻优速度的同时防止陷入局部最优;交叉前后的种群分别实施精英个体保留策略,交叉变异之后引进进化逆转操作,继承父代较优和较多的信息.实验结果表明:改进遗传算法搜索效率高、计算结果较为稳定;求解车辆路径最优问题较其它算法具有较好的性能.
On the basis of the analysis of the vehicle routing problem, it is transformed into the classical TSP optimization problem to solve and build a mathematical model. Standard genetic algorithm in solving the vehicle routing problem （VRP） is not efficient since it is easy to fall local optimum. To improve the efficiency of genetic algorithm, this paper presents an improved genetic algorithm. The proposed algorithm introduces the adaptive neighborhood method into population initialization. In order to improve the optimization speed and prevent the local optimum, the improved algorithm dynamic adaptive adjustment of genetic parameters based on predatory search strategy. Elite individual retention strategy is introduced to genetic operation and evolutionary reversal operation is used after crossover and mutation operation to propagate the better and more gene structure. The experimental results show that the improved genetic algorithm has high search efficiency and stable calculation results, and it has better performance than other algorithms in case of solving the vehicle routing problem.
出处
《中南民族大学学报（自然科学版）》
CAS
北大核心
2016年第4期106-110,共5页
Journal of South-Central University for Nationalities：Natural Science Edition
基金
天津市科技支撑项目(14ZCDZGX00818)
关键词
车辆路径问题
遗传算法
自适应邻域法
捕食搜索算法
vehicle routing problem
genetic algorithm
adaptive neighborhood method
predatory search algorithm
