关键词: 粒子群算法, 惯性权重, 最小二乘支持向量机, 客流量预测

Abstract: The accurate prediction of tourist flow provides an effective basis for optimizing the allocation of tourism resources and the development of a strategic plan. In order to improve the forecasting accuracy of daily tourist flow, an improved prediction method of Least Squares Support Vector Machine（LSSVM） optimized by modified Particle Swarm Optimization（PSO） is proposed. In the improved PSO algorithm, considering the degree of aggregation of particles and the relationship between the value of particle fitness and inertia weight in population evolution, in the view of the nonlinear variation of the logarithmic function, an Adaptive Logarithmic Particle Swarm Optimization（ALPSO） is proposed since when the inertia weight adopts a typical linear decrement strategy can not satisfied the defect of non-linear variation characteristics of particle optimization. A prediction model of short-term tourist flow in mountainous scenic spot is established that the modified PSO algorithm is used to optimize the parameters of LSSVM. Given the daily tourist data of Mountain Huangshan from 2012 to 2015 as an example, the results of experiments corroborate that compared with those using LSSVM with standard PSO, LSSVM with Sinusoidal Particle Swarm Optimization（SPSO） and LSSVM with Gaussian Particle Swarm Optimization（GPSO）, the ALPSO-LSSVM model presents better predictive performance which is an effective method for accurately forecasting daily tourist flow in scenic areas.

Key words: particle swarm optimization, inertia weight, least squares support sector machine, tourism flow forecasting