Solving the Tourist Planning Problem Using a Hybrid Genetic Tabu Algorithm

The goal of this work is to apply metaheuristics to the problem of planning tourist trips. The tourist trip planning problem is the preparation of an optimal personalized itinerary based on a maximization of the tourists' preference factors while respecting the time constraint, provided that a tourist site is visited only once. This problem can be modeled as an Orientation Problem and its variants. To achieve our goal, we propose a Hybrid Genetic Tabu algorithm. In our algorithm, we use genetic operators such as the crossover operator, which is adopted for the variable-size chromosome. In mutation, the exchange operator and the insertion operator are used, and then we improve the solution found by adding a tabu search. This approach takes advantage of the complementarity of hybrid methods. The results obtained are discussed and compared with other known results in the literature. The computational results demonstrate the efficacy and efficiency of our proposed algorithm.