Orama, Jonathan Ayebakuro; Moreno, Antonio; Borras, Joan (2022). Multi Objective Genetic Algorithm for Optimal Route Selection from a Set of Recommended Touristic Activities. Amsterdam: IOS Press
Papper original source:
Frontiers In Artificial Intelligence And Applications. 356 9-12
Abstract:
It is a known fact that the order in which touristic activities are experienced plays a role in how enjoyable they are. This is the reason why tourists prefer to book carefully prepared day tours on arrival to a new destination, as they allow them to see the essence of the destination while traversing scenic routes. Tours are great, but they are expensive, do not allow room for personal exploration, and are built as a one-size-fits-all which does not consider the individual preferences of the tourist. In contrast, it is possible to make an optimal selection and ordering of touristic activities from a larger set of possibilities that match a tourist's personal preferences, balancing important aspects like diversity, spatial proximity, or degree of interest on popular places. We propose a multi-objective genetic algorithm that uses a weighted averaging operator to balance four diverse objective functions crafted to maintain diversity, proximity, interest on popularity, and cultural preference. The system has been evaluated against four baseline algorithms and found to perform significantly better for the specified purpose.
It is a known fact that the order in which touristic activities are experienced plays a role in how enjoyable they are. This is the reason why tourists prefer to book carefully prepared day tours on arrival to a new destination, as they allow them to see the essence of the destination while traversing scenic routes. Tours are great, but they are expensive, do not allow room for personal exploration, and are built as a one-size-fits-all which does not consider the individual preferences of the tourist. In contrast, it is possible to make an optimal selection and ordering of touristic activities from a larger set of possibilities that match a tourist's personal preferences, balancing important aspects like diversity, spatial proximity, or degree of interest on popular places. We propose a multi-objective genetic algorithm that uses a weighted averaging operator to balance four diverse objective functions crafted to maintain diversity, proximity, interest on popularity, and cultural preference. The system has been evaluated against four baseline algorithms and found to perform significantly better for the specified purpose.
Artificial Intelligence Multi-objective genetic algorithm Travel route optimization Weighted average objective balancin Weighted average objective balancing Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii