摘要
目的设计一种改进的蚁群算法求解带容量约束的车辆路径问题(Capacitated Vehicle Routing Problem,CVRP)。方法使用改进的扫描法进行信息素初始化,同时采用了基于罚函数的适应度函数。结果与结论从4个CVRP数据集中选取了不同规模的41个实例进行了参数设置和对比实验,改进蚁群算法性能优于基本的蚁群算法,具有较强的寻优能力,能够有效求解带容量约束的车辆路径问题。
Purposes—To develop an improved ant colony algorithm for solving a capacitated vehicle routing problem(CVRP).Methods—An improved scanning method is used to initialize the pheromone,meanwhile a penalty function-based fitness function is employed as well.Result and Conclusion—Parameter setting experiments and comparison experiments on 41 instances from 4 CVRP datasets show that the improved ant colony algorithm not only outperforms the basic ant colony algorithm but also has a better optimization ability and can solve the capacitated vehicle routing problem effectively.
作者
张航
高岳林
ZHANG Hang;GAO Yue-lin(Collage of Computer Science and Engineering,North Minzu University,Yinchuan 750021,Ningxia,China;Collage of Mathematics and Information Science,North Minzu University,Yinchuan 750021,Ningxia,China;Ningxia Key Laboratory of Intelligence Information and Big Data Processing,Yinchuan 750021,Ningxia,China)
出处
《宝鸡文理学院学报（自然科学版）》
CAS
2022年第3期18-23,29,共7页
Journal of Baoji University of Arts and Sciences(Natural Science Edition)
基金
国家自然科学基金项目(11961001)
宁夏自然科学基金重点研究项目(2022AAC02043)
宁夏高等教育一流学科教育基金项目(NXYLXK2017B09)
北方民族大学重大科研专项(ZDZX201901)
北方民族大学研究生创新项目(YCX21095)。
关键词
蚁群算法
车辆路径问题
信息素
适应度函数
ant colony algorithm
vehicle routing problem
pheromone
fitness function
