An interactive preference-guided firefly algorithm for personalized tourist itineraries

Summarization: The present research proposes an interactive optimization framework to aid tourists to organize their trip by generating personalized walking itineraries among several Points of Interest (POIs). The solution of the multi-objective Prize-Collecting Vehicle Routing Problem (MO-PCVRP) is used to simulate this tourist trip design problem. The objectives of the proposed formulation are the minimization of the total distance walked among selected POIs, the minimization of a fixed cost related to the number of the created itineraries, and the maximization of the total satisfaction gained by visiting the selected POIs. The optimization of the MO-PCVRP is conducted by the proposed Preference-Guided Firefly Algorithm (PGFA), which allows for preferences articulated by a decision-maker (DM) to guide the search. The PGFA is incorporated into an interactive framework, where a DM provides his/her preferential information, progressively during the optimization process, by ranking a small representative set of Pareto optimal solutions. The DM’s articulated preferences are elicited utilizing a preference disaggregation method, the UTASTAR, which results in a preference model, which is ultimately used to guide the search towards the DM’s Region of Interest (ROI) in the Pareto front. The effectiveness and robustness of the proposed interactive PGFA framework are demonstrated over experimental scenarios.Presented on: Expert Systems with Application