改进果蝇算法优化回声状态网络的旅游需求预测研究

Tourism demand prediction using echo state network with improved fruit fly optimization algorithm

首先对标准果蝇优化算法FOA进行改进,自适应调整果蝇种群数量和搜索步长,同时优化初始迭代位置,改善算法局部搜索能力和搜索效率。接着将改进的FOA算法AFOA与回声状态网络ESN相结合,构建一个两阶段组合预测模型(AFOA-ESN),通过AFOA优化ESN获取其关键参数,将优化后的参数输入ESN,形成最终的组合预测模型。最后利用该模型进行旅游需求预测。实验结果表明,AFOA-ESN模型较自回归移动平均模型、支持向量机模型、BP神经网络、标准ESN网络以及其他预测模型具有更高的预测精度。

Firstly,this paper improves the standard Fruit Fly Optimization Algorithm(FOA)by adaptively adjusting the number of the fruit fly populations and the search step size and optimizing the initial iteration position,so as to improve the local search ability and search efficiency of the algorithm.Through combining the optimized FOA(AFOA)and Echo State Network(ESN),a two-stage combined prediction model(AFOA-ESN)is proposed.The AFOA optimizes the ESN to obtain its key parameters,which are inputted into the ESN to form the final combined prediction model.Finally,this model is used to predict tourism demand.The experimental results show that the AFOA-ESN model has higher prediction accuracy than the ARIMA,SVM,BPNN,standard ESN and other models.