The accurate forecasting of monthly tourism demand can improve tourism policies and planning. However, the complex nonlinear characteristics of monthly tourism demand complicate forecasting. This study proposes a novel approach named ICPSO-BPNN that combines improved chaotic particle swarm optimization (ICPSO) with backpropagation neural network (BPNN) to forecast monthly tourism demand. ICPSO with chaotic initialization and two search strategies, sigmoid-like inertia weight, and linear acceleration coefficients is utilized to search for the appropriate initial connection weights and thresholds necessary to improve the performance of BPNN. Two comparative real-life examples and one extended example are adopted to verify the superiority of the proposed ICPSO-BPNN. Results show ICPSO-BPNN outperforms that of the basic BPNN, autoregressive integrated moving average model, support vector regression, and other popular existing models.

对月度旅游需求的准确预测可以改善旅游政策和规划。但是，月度旅游需求的复杂非线性特征使预测变得复杂。这项研究提出了一种名为ICPSO-BPNN的新方法，该方法结合了改进的混沌粒子群优化（ICPSO）和反向传播神经网络（BPNN）来预测每月的旅游需求。具有混沌初始化和两种搜索策略（类似于S形惯性权重和线性加速度系数）的ICPSO用于搜索适当的初始连接权重和阈值，以改善BPNN的性能。采用两个比较现实的例子和一个扩展例子来验证所提出的ICPSO-BPNN的优越性。结果表明ICPSO-BPNN优于基本BPNN，