1. Home
  2. Vol 4, No 2 (2023)
  3. Wang

Study of Optimization of Tourists' Travel Paths by Several Algorithms

Ting Wang

Abstract


The purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments.

 

Doi: 10.28991/HIJ-2023-04-02-012

Full Text: PDF


Keywords


Tourism; Path Planning; Genetic Algorithm; Particle Swarm Optimization; Ant Colony Algorithm.

References


Yu, C., & Zhang, H. (2020). Research on experiential tourism route planning based on multi-source data algorithm in rich energy and cultural resources areas. Journal of Physics: Conference Series, 1648(2), 1–5. doi:10.1088/1742-6596/1648/2/022034.

Dayoub, B., Yang, P., Dayoub, A., Omran, S., & Li, H. (2020). The role of cultural routes in sustainable tourism development: A case study of Syria’s spiritual route. International Journal of Sustainable Development and Planning, 15(6), 865–874. doi:10.18280/ijsdp.150610.

Qu, Z. (2020). Construction of Tourism Planning Information System Based on Ant Colony Algorithm. Journal of Physics: Conference Series, 1533(2), 022101. doi:10.1088/1742-6596/1533/2/022101.

Wu, X., Guan, H., Han, Y., & Ma, J. (2017). A tour route planning model for tourism experience utility maximization. Advances in Mechanical Engineering, 9(10). doi:10.1177/1687814017732309.

Zhang, Y., Jiao, L., Yu, Z., Lin, Z., & Gan, M. (2020). A Tourism Route-Planning Approach Based on Comprehensive Attractiveness. IEEE Access, 8, 39536–39547. doi:10.1109/ACCESS.2020.2967060.

Zhu, Y., & Lan, S. (2020). Key Route Planning Models of Natural Hot Spring Tourism in Coastal Cities. Journal of Coastal Research, 103(sp1), 1084. doi:10.2112/SI103-226.1.

Khamsing, N., Chindaprasert, K., Pitakaso, R., Sirirak, W., & Theeraviriya, C. (2021). Modified ALNS Algorithm for a Processing Application of Family Tourist Route Planning: A Case Study of Buriram in Thailand. Computation, 9(2), 23. doi:10.3390/computation9020023.

Hirano, M., & Yamamoto, K. (2022). Food Tourism Planning Support System within Urban Sightseeing Areas in Japan. Journal of Geographic Information System, 14(05), 389–409. doi:10.4236/jgis.2022.145021.

Chen, C., Zhang, S., Yu, Q., Ye, Z., Ye, Z., & Hu, F. (2021). Personalized travel route recommendation algorithm based on improved genetic algorithm. Journal of Intelligent & Fuzzy Systems, 40(3), 4407–4423. doi:10.3233/jifs-201218.

Xu, Y., Guo, Q., Tan, A., Xu, L., Tu, Y., & Liu, S. (2021). Multi-objective Route Planning of Museum Guide based on an Improved NSGA-II Algorithm. Journal of Physics: Conference Series, 1828(1), 012051. doi:10.1088/1742-6596/1828/1/012051.

Zhang, H., Guo, T., & Su, X. (2021). Application of Big Data Technology in the Impact of Tourism E-Commerce on Tourism Planning. Complexity, 2021, 1–10. doi:10.1155/2021/9925260.

Damos, M. A., Zhu, J., Li, W., Hassan, A., & Khalifa, E. (2021). A novel urban tourism path planning approach based on a multiobjective genetic algorithm. ISPRS International Journal of Geo-Information, 10(8). doi:10.3390/ijgi10080530.

Xiao, Z., Sen, L., Yunfei, F., Bin, L., Boyuan, Z., & Bang, L. (2017). Tourism Route Decision Support Based on Neural Net Buffer Analysis. Procedia Computer Science, 107, 243–247. doi:10.1016/j.procs.2017.03.086.

Milošević, P., Milošević, V., & Milošević, G. (2022). Investigation Architecture and Environmental Planning in Prehistory for Designing an Ecologically Sustainable Tourist Resort. Journal of Human, Earth, and Future, 3(1), 99-128. doi:10.28991/HEF-2022-03-01-08.

Mei, Y. (2018). Study on the application and improvement of ant colony algorithm in terminal tour route planning under Android platform. Journal of Intelligent and Fuzzy Systems, 35(3), 2761–2768. doi:10.3233/JIFS-169628.

Zhou, X., Su, M., Liu, Z., & Zhang, D. (2019). Smart tour route planning algorithm based on clustering center motive iteration search. IEEE Access, 7, 185607–185633. doi:10.1109/ACCESS.2019.2960761.

Wang, J. (2019). Research on the optimization of path information in the process of logistics distribution by improved ant colony algorithm. Italian Journal of Pure and Applied Mathematics, 2019(41), 343-352.

Anand, A., & George, V. (2022). Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS. Civil Engineering Journal, 8(9), 1936-1957. doi:10.28991/CEJ-2022-08-09-013.

Theocharis, N., Leligou, H. C., & Tseles, D. (2022). Innovation for People with Disabilities in Hospitality Industry: A Theoretical Approach. HighTech and Innovation Journal, 3(1), 102-114. doi:10.28991/HIJ-2022-03-01-010.

Surono, S., Goh, K. W., Onn, C. W., Nurraihan, A., Siregar, N. S., Saeid, A. B., & Wijaya, T. T. (2022). Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Emerging Science Journal, 6(6), 1375-1393. doi:10.28991/ESJ-2022-06-06-010.

Wang, Y., Zhou, H., & Wang, Y. (2017). Research and application of genetic algorithm in path planning of logistics distribution vehicle. AIP Conference Proceedings. doi:10.1063/1.4992864.

Simić, D., Kovačević, I., Svirčević, V., & Simić, S. (2014). Hybrid firefly model in routing heterogeneous fleet of vehicles in logistics distribution. Logic Journal of the IGPL, 23(3), 521–532. doi:10.1093/jigpal/jzv011.

Gu, W., Liu, Y., Wei, L. R., & Dong, B. K. (2015). A hybrid optimization algorithm for traveling salesman problem based on geographical information system for logistics distribution. International Journal of Grid and Distributed Computing, 8(3), 359–370. doi:10.14257/ijgdc.2015.8.3.33.


Full Text: PDF

DOI: 10.28991/HIJ-2023-04-02-012

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Ting Wang