Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231200072-8.doi: 10.11896/jsjkx.231200072

• Intelligent Computing • Previous Articles     Next Articles

Tourism Route Planning Based on Colored Traveling Salesman Problem

GU Chan, FU Yan, YE Shengli   

  1. School of Electrical and Control Engineering,Shaanxi University of Science ＆ Technology,Xi'an 710021,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:GU Chan,born in 1990,Ph.D,assoicate professor.Her main research interests include supervisory control of discrete event systems,state-tree structures,automata,and observability of discrete event systems.
  • Supported by:
    National Natural Science Foundation of China(62003201).

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Abstract: Studying the tourism route planning problem is crucial to promote the development of urban tourism and improve tou-rists' experience,which is usually explored as a coloring traveler′s problem in abstraction by researchers.However,in solving the optimal routes for large-scale attractions,existing methods suffer from poor solution flexibility and slow convergence speed.Therefore,the coloring traveler problem and its application in tourism route planning are explored based on automata.Firstly,the roadmap of attractions is established by automata,and decentralized discussion is carried out by using the hierarchical automata method.Secondly,the selection of attractions is flexibly handled,and invalid paths are deleted by using its structural properties.Finally,the shortest route is solved by using ant colony algorithm on the simplified model.The experiment selects the attractions in and around Xi'an city as the sample data,and the research results show that,compared with the traditional ant colony algorithm and simulated annealing algorithm,the proposed algorithm reduces the complexity of the problem,searches within the effective range,and can converge and find the shortest path within 20 iterations,and it can also flexibly plan the reasonable tourist route according to the tourists' personalized needs.

Key words: Colored traveling salesman problem, Ant colony algorithm, Automaton, Tour route planning

CLC Number: 

  • TP301
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