Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

To accurately predict the development and change trend of future, tourism market can effectively improve planning purpose development. In order to accuracy tourist demand prediction, this paper studies prediction model based on improved fruit fly algorithm. Aiming at optimization defects traditional algorithm (FOA), introduces two concepts sensitivity pheromone, improves strategy position replacement fly, diversity population, modifies global characteristics algorithm, local search ability efficiency By combining AFOA with echo state network (ESN), a two-stage combined (AAFOA-ESN) is constructed. The experimental results show that minimum error only 0.55%, which has more robust effect, faster convergence speed, higher accuracy.