Personalized itinerary planning using an adaptive genetic algorithm

Abstract

Planning an itinerary for travelling can be tedious, time-consuming, and challenging. This is especially true for tourists who have limited time budgets and are unfamiliar with a wide range of Points-of-Interest (POIs) in a city. To address this challenge, this paper proposes an Adaptive Genetic Algorithm (AGA) for personalized itinerary planning. This approach considers travelers' preferences, such as mandatory POIs, total number of POIs, POI popularity, POI cost, and POI rating. It views the itinerary planning problem as a multi-objective optimization problem and proposes an Adaptive Genetic Algorithm (AGA) to solve this problem. The results show that the AGAM algorithm is a promising approach for personalized itinerary planning. It is able to find itineraries that meet the traveler's preferences that are efficient in terms of time, cost, and overall rating.