The paper presents a novel optimizer to plan multiple-day walking itineraries, tailored to tourists' personal interests, in a street network modeled as a graph. The tour is automatically designed by maximizing the number of the Points of Interest (POI s) to visit as a function of both tourists' preferences and requirements, and constraints such as opening hours, visiting times and accessibility of the POI s, and weather forecasting. Since this itineray planning is classified as an NP-complete combinatorial optimization problem, a multiobjective evolutionary optimizer is here proposed. Such an optimizer is proven to be effective in designing personalized multiple-day tourist routes.
A multiobjective evolutionary algorithm for personalized tours in street networks
De Falco I;Scafuri U;Tarantino E
2015
Abstract
The paper presents a novel optimizer to plan multiple-day walking itineraries, tailored to tourists' personal interests, in a street network modeled as a graph. The tour is automatically designed by maximizing the number of the Points of Interest (POI s) to visit as a function of both tourists' preferences and requirements, and constraints such as opening hours, visiting times and accessibility of the POI s, and weather forecasting. Since this itineray planning is classified as an NP-complete combinatorial optimization problem, a multiobjective evolutionary optimizer is here proposed. Such an optimizer is proven to be effective in designing personalized multiple-day tourist routes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
