Abstract
The itinerary planning problem plays a pivotal role in the tourism industry, involving the selection of an optimal tour route from multiple preferred points of interest (POIs) chosen by travelers while considering their diverse needs. However, as tourism expands and transportation becomes more accessible, there is a growing preference among travelers for planning single trips across multiple cities-referred to as cross-city itinerary planning. This paper introduces a novel approach, called CCIP, the cooperative coevolution framework for cross-city itinerary planning, which employs a divide-and-conquer method to automatically devise scalable cross-city itineraries, accounting for travelers’ preferences regarding time and travel choices. Experimental evaluations on real datasets from various cities in Jiangsu Province demonstrate that the proposed algorithm outperforms two classical multi-objective optimization algorithms, as measured by the HV metric.
This work is partly supported by Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 23KJB520018), the Startup Foundation for Introducing Talent of NUIST (Grant No. 2022r121), the Natural Science Foundation of Jiangsu Province (Grant No. BK20230419).
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Zhang, Z., Xu, P., Wang, Z., Luo, W. (2024). Cooperative Coevolution for Cross-City Itinerary Planning. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_28
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