基于自适应动态搜索蚁群算法的车辆路径规划  

Vehicle path planning based on adaptive dynamic search ant colony algorithm

作　　者：贺智明[1] 郑丽 梁文 HE Zhi-ming;ZHENG Li;LIANG Wen(School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学信息工程学院,江西赣州341000

出　　处：《计算机工程与设计》2021年第2期543-551,共9页Computer Engineering and Design

基　　金：江西省教育厅科学技术研究基金项目(GJJ170518);江西省研究生创新专项基金项目(YC2018-S331)。

摘　　要：基于蚁群算法求解车辆路径规划的缺陷性分析,提出一种自适应动态搜索蚁群算法(ADACO)。建立算法模型,以测试案例的TSP问题为基础实验性配置组合参数;采用伪随机分布和自适应转移概率相结合策略,帮助群体选择较高质量路径;分段化设定信息素强度,有效诱导群体及时跳脱局部困境并构造新的解。测试结果表明,无论在时间开销还是配送成本方面,ADACO算法较于其它算法均有实质性地突破,验证了该算法的可行性。Based on the defect analysis of ant colony algorithm in vehicle path planning,an adaptive dynamic search ant colony search algorithm(ADACO)was proposed.The algorithm model was established and the combination parameters were experimentally configured based on the TSP problem of the test case.The combination of pseudo-random distribution and adaptive transfer probability was adopted to help the group select a higher quality path.The segmented pheromone intensity effectively induced the group to jump out of the local dilemma and construct a new solution.The test results show that ADACO algorithm has a substantial breakthrough compared with other algorithms in terms of time consumption and distribution cost,which fully verifies the feasibility of the algorithm.

关 键 词：蚁群算法 车辆路径规划 伪随机分布 自适应转移概率 分段函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]