A Hybrid Framework Model Based on Wavelet Neural Network with Improved Fruit Fly Optimization Algorithm for Traffic Flow Prediction

Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit fly optimization algorithm (FOA) is easy to fall into local optimum and the search method is single, an improved fruit fly optimization algori...

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Vydáno v:Symmetry (Basel) Ročník 14; číslo 7; s. 1333
Hlavní autoři: Zhang, Qingyong, Li, Changwu, Yin, Conghui, Zhang, Hang, Su, Fuwen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2022
Témata:
Accuracy
Algorithms
Cooperation
Experiments
Foraging behavior
Intelligence
Iterative methods
Mathematical models
Neural networks
Optimization
Optimization algorithms
Parameters
Prediction models
Roads & highways
Search methods
Time series
Traffic congestion
Traffic flow
Traffic information
Vehicles
ISSN:2073-8994, 2073-8994
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Shrnutí:Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit fly optimization algorithm (FOA) is easy to fall into local optimum and the search method is single, an improved fruit fly optimization algorithm (IFOA) based on parallel search strategy and group cooperation strategy is proposed. The multi-swarm mechanism is introduced in the parallel search strategy, in which each subswarm is independent and multiple center positions are determined in the iterative process, thereby avoiding the problems of reduced diversity and premature convergence. To increase communication between fruit fly subswarms, the informative fruit flies selected from subswarms are guided by the randomly generated binary fruit fly to achieve the crossover operation in the group cooperation strategy. Then a hybrid framework model based on wavelet neural network (WNN) with IFOA (IFOA-WNN) for traffic flow prediction is designed, in which IFOA is applied to explore appropriate structure parameters for WNN to achieve better prediction performance. The simulation results verify that the IFOA can provide high-quality structural parameters for WNN, and the hybrid IFOA-WNN prediction model can achieve higher prediction accuracy and stability than the compared methods.
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content type line 14
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14071333