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

中文翻译：

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基于改进果蝇算法的旅游需求预测模型

准确预测未来的发展变化趋势，旅游市场可以有效提高旅游发展的规划和目的。为了提高旅游需求预测的准确性，本文研究了基于改进果蝇算法的旅游需求预测模型。该模型针对传统果蝇优化算法（FOA）的优化缺陷，引入了灵敏度和信息素两个概念，改进了果蝇的优化策略和位置替换，提高了果蝇种群的多样性，修改了全局优化特征提高了算法的局部搜索能力和搜索效率。通过将改进的 AFOA 与回声状态网络 (ESN) 相结合，构建了一个两阶段组合预测模型（AAFOA-ESN）。实验结果表明，该模型的最小预测误差精度仅为0.55%，预测效果更稳健，收敛速度更快，预测精度更高。