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基于分布均匀度自适应蚁群算法的自驾游路线规划研究 认领

Research on Self-Driving Route Planning Based on Distribution Uniformity Adaptive Ant Colony Algorithm
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摘要 提出了一种基于分布均匀度自适应蚁群算法的自驾游旅行方案设计。蚁群算法是一种用来在图中寻找优化路径的机率型模拟进化算法,引入优化过程中解的分布均匀度,动态地调整信息量更新策略和选择路径概率,可加速收敛的同时避免早熟,得到更为恰当的结果。本文以苏州21个当地景点为例,通过使用改进后的蚁群算法,得出了一条较合理的自驾游路线。通过本文的研究,能推广基于分布均匀度的自适应蚁群算法在旅游线路规划中的应用,为自驾游路线规划提供合理解决方案。 An adaptive ant colony algorithm based on distribution uniformity was proposed for the design of self-driving travel scheme.Ant colony algorithm(ACO)is a probabilistic simulated evolutionary algorithm used to find the optimal path in the graph.By introducing the distribution uniformity of solutions in the optimization process and dynamically adjusting the information updating strategy and path selection probability,it can accelerate the convergence and avoid the prematurity to get more appropriate results.Taking 21 local scenic spots in Suzhou as examples,this paper USES the improved ant colony algorithm to get a more reasonable self-driving tour route.Through the study of this paper,the application of adaptive ant colony algorithm based on distribution uniformity in tourism route planning can be popularized,and a reasonable solution can be provided for self-driving route planning.
作者 宋明杰 吴宇航 SONG Ming-jie;WU Yu-hang(College of Science,North China University of Science and Technology,Hebei,Tangshan,China 063210;Office of Educational Administration,North China University of Science and Technology,Hebei,Tangshan,China 063210;Hebei key laboratory of data science and application,Hebei,Tangshan,China 063210)
出处 《新一代信息技术》 2020年第6期10-17,共8页 New Generation of Information Technology
基金 2020年河北省级研究生示范课程立项建设项目(项目编号:KCJSX2020053)。
关键词 分布均匀度 蚁群算法 最短路径 旅游线路规划 Distribution uniformity Ant colony algorithm Shortest path Tourist route planning
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