基于蚁群算法的多目标最优旅游线路规划设计  

Multi-objective optimal tourism itinerary planning and design:The application of ant colony algorithm

作　　者：翟淞 吕宁 李烨 房俊晗 Zhai Song;Lyu Ning;Li Ye;Fang Junhan(Shandong Vocational College of Light Industry,Zibo 255300,China;Shandong Management University,Jinan 250300,China)

机构地区：[1]山东轻工职业学院,淄博255300 [2]山东管理学院,济南250300

出　　处：《中国生态旅游》2022年第5期848-860,共13页Journal of Chinese Ecotourism

基　　金：山东省人文社会科学基金项目(2020-NDJJ-09)。

摘　　要：旅游线路受供需双方旅行成本、旅游者体验感和目的地交通的影响。传统旅游线路设计主要采用基于目的地之间空间距离的蚁群算法。本研究结合帕累托最优模型,综合考虑旅游目的地之间的空间距离、天气状况和交通状况等多种因素,提出改进型蚁群算法的多目标最优旅游线路规划设计方法。通过MATLAB软件进行仿真实验进行检验,固定旅行时长,从空间距离、交通体验指数和游览体验指数(基于天气状况)角度对改进后的方法进行综合评价。结果表明:在以多个旅游城市(景点)为对象的试验中,传统蚁群算法为了获得最短距离,大量牺牲了其他两项指数的优化;改进后的蚁群算法虽然增加了线路距离,但使旅游者获得了多项旅游体验,验证了帕累托最优模型下多目标最优旅游线路规划设计的有效性。Travel itinerary is affected by the spatial distance between destinations,tourism experience and traffic conditions.The traditional travel itinerary design is mainly based on the spatial distance between destinations with the application of ant colony algorith.Based on ant colony algorithm and Pareto optimal model,this paper constructs a multi-objective optimal tourism route planning and design method considering the factors of spatial distance between destinations,weather conditions and transportation.Through MATLAB simulation experiment,with the fixed travel time,the improved method is comprehensively evaluated from the perspectives of spatial distance,traffic experience index and browsing experience index(based on weather conditions).The results show that in the experiments of multiple tourist cities(scenic spots),the traditional ant colony algorithm sacrifices a lot of optimization of the two indexes in order to obtain the shortest distance.Although the improved ant colony algorithm increases the route distance,tourists can obtain more tourism experiences,which verifies the effectiveness of multi-objective optimal tourism itinerary planning and design under the Pareto optimal model.

关 键 词：旅游线路设计 传统蚁群算法 改进型蚁群算法 帕累托最优模型 

分 类 号：F592[经济管理—旅游管理] TP18[经济管理—产业经济]