scholarly journals Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chuangle Guo ◽  
Wei Shang
Keyword(s):  
Prediction Model ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Optimization Strategy ◽  
Fruit Fly Optimization ◽  
Demand Prediction ◽  
Model Based ◽  
Change Trend ◽  
Tourist Demand ◽  
Robust Prediction

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.

scholarly journals A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hong Yang ◽  
Siliang Wang ◽  
Guohui Li ◽  
Tongtong Mao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Acoustic Signal ◽  
Fruit Fly ◽  
Wavelet Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Underwater Acoustic ◽  
Fruit Fly Optimization ◽  
Neural Network Prediction ◽  
Underwater Acoustic Signal

The local predictability of underwater acoustic signal plays an important role in underwater acoustic signal processing, and it is the basis of nonstationary signal detection. Wavelet neural network model, with the advantages of both wavelet analysis and artificial neural network, makes full use of the time-frequency localization characteristics of wavelet analysis and the self-learning ability of artificial neural network; however, this model is prone to fall into local minima or creates convergence. To overcome these disadvantages, a new hybrid model based on fruit fly optimization algorithm (FOA) and wavelet neural network (WNN) is proposed in this paper. The FOA-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction. The experimental results show that the FOA-WNN prediction model has higher prediction accuracy and smaller prediction error, compared with wavelet neural network prediction model and BP neural network prediction model.

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