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Volume 10 Issue 2
Feb.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(2): 486-500
X. Y. Zheng, B. T. Han, and Z. Ni, “Tourism route recommendation based on a multi-objective evolutionary algorithm using two-stage decomposition and Pareto layering,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 486–500, Feb. 2023. doi: 10.1109/JAS.2023.123219
Citation: X. Y. Zheng, B. T. Han, and Z. Ni, “Tourism route recommendation based on a multi-objective evolutionary algorithm using two-stage decomposition and Pareto layering,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 486–500, Feb. 2023. doi: 10.1109/JAS.2023.123219
shu

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

doi: 10.1109/JAS.2023.123219
  • 1.

    School of Computer and Information, Anhui Normal University, Wuhu 241002, China

  • 2.

    Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton FL33431 USA

Funds:  This work was partially supported by the National Natural Science Foundation of China (41930644, 61972439), the Collaborative Innovation Project of Anhui Province (GXXT-2022-093), and the Key Program in the Youth Elite Support Plan in Universities of Anhui Province (gxyqZD2019010)
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    Abstract
  • Tourism route planning is widely applied in the smart tourism field. The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails, sharp peaks and disconnected regions problems, which leads to uneven distribution and weak diversity of optimization solutions of tourism routes. Inspired by these limitations, we propose a multi-objective evolutionary algorithm for tourism route recommendation (MOTRR) with two-stage and Pareto layering based on decomposition. The method decomposes the multi-objective problem into several subproblems, and improves the distribution of solutions through a two-stage method. The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method. The neighborhood is determined according to the weight of the subproblem for crossover mutation. Finally, Pareto layering is used to improve the updating efficiency and population diversity of the solution. The two-stage method is combined with the Pareto layering structure, which not only maintains the distribution and diversity of the algorithm, but also avoids the same solutions. Compared with several classical benchmark algorithms, the experimental results demonstrate competitive advantages on five test functions, hypervolume (HV) and inverted generational distance (IGD) metrics. Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing, our proposed algorithm shows better distribution. It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity, so that the recommended routes can better meet the personalized needs of tourists.

     

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    Highlights

    • The crowding degree mechanism can determine the boundary region and the intermediate solution in multi-objective evolutionary algorithm, meanwhile, it combine inverse scalar sub-problem to complete the extreme distribution optimization. This method can improve the solution’s distribution of tourism route recommendation
    • A strategy for dividing sub regions Pareto layering structure, dnd using the penalty-based boundary intersection values in the sub-regions can improve the diversity of solutions
    • Combining the two-stage approach with pareto layering, the distribution and diversity of the solutions for multi-objective evolutionary algorithm, which can effectively improve the service quality of travel route recommendation

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