基于IMPA-RELM的旅游景点客流量预测研究

Research on Tourist Flow Prediction Based on IMPA-RELM

旅游景点客流量预测是旅游管理领域的重要研究问题,关乎着旅游政策制定和旅游景区经营管理。提出了一种基于改进海洋捕食者算法优化正则化极限学习机的旅游景点客流量预测方法。首先,为自适应地平衡探索与开发状态,提出一种基于群体多样性和群体聚集度的海洋捕食者算法,充分发挥MPA算法探索与开发性能。其次,将改进的海洋捕食者算法用于优化正则化极限学习机(IMPA-RELM)的权重与偏置,以归一化均方根误差作为适应度函数,确定最佳权重和偏置参数。最后,将所构建的IMPA-RELM模型应用于九寨沟和查干湖景区单日客流量预测研究。实验结果表明,所提出的IMPA-RELM模型不仅显著提升了RELM的模型性能,相比于LS-SVM、BPNN和LSTM等基线模型,也具有更强的预测性能与泛化能力,能够为景区运营管理和旅游政策制定提供重要参考。

Tourist flow forecasting is an important research problem in the field of tourism management,which is related to the formulation of tourism policy and the management of tourist attractions.In this paper,a method for predicting the tourist flow of tourist attractions based on the improved marine predator algorithm(MPA)optimization regularized extreme learning machine is proposed.First,in order to adaptively balance the exploration and exploitation status,this paper proposes a MPA based on population diversity and population aggregation,which gives full play to the exploration and exploitation performance of MPA algorithm.The IMPA optimizes the weight and bias of the regularized extreme learning machine(IMPA-RELM),and uses the normalized root mean square error as the fitness function to determine the optimal weight and bias parameters.Finally,the built IMPA-RELM model is applied to the prediction of daily tourist flow in Jiuzhaigou and Chagan Lake scenic spots.The experimental results show that the proposed IMPA-RELM model not only significantly improves the performance of the RELM model,but also has more superior prediction performance and generalization ability compared with the baseline models such as LS-SVM,BPNN and LSTM.Thus,the novel method can provide important reference for the operation and management of scenic spots and the formulation of tourism policies.