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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| Published in: | Symmetry (Basel) Vol. 14; no. 7; p. 1333 |
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| Main Authors: | , , , , |
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| Language: | English |
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01.07.2022
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| ISSN: | 2073-8994, 2073-8994 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Zhang, Hang Li, Changwu Su, Fuwen Yin, Conghui Zhang, Qingyong |
| Author_xml | – sequence: 1 givenname: Qingyong surname: Zhang fullname: Zhang, Qingyong – sequence: 2 givenname: Changwu orcidid: 0000-0002-1407-8875 surname: Li fullname: Li, Changwu – sequence: 3 givenname: Conghui surname: Yin fullname: Yin, Conghui – sequence: 4 givenname: Hang surname: Zhang fullname: Zhang, Hang – sequence: 5 givenname: Fuwen surname: Su fullname: Su, Fuwen |
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| Snippet | Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit... |
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| SubjectTerms | 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 |
| Title | A Hybrid Framework Model Based on Wavelet Neural Network with Improved Fruit Fly Optimization Algorithm for Traffic Flow Prediction |
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