A study on muti-strategy predator algorithm for passenger traffic prediction with big data
In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0681 |
| _version_ | 1797229594089095168 |
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| author | Fu Yujie Gao Ming Zhu Xiaohui Fu Jihong |
| author_facet | Fu Yujie Gao Ming Zhu Xiaohui Fu Jihong |
| author_sort | Fu Yujie |
| collection | DOAJ |
| description | In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry. |
| first_indexed | 2024-04-24T15:15:04Z |
| format | Article |
| id | doaj.art-8a07cb0a6fa14ba99a8b1c86db55e46d |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-04-24T15:15:04Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-8a07cb0a6fa14ba99a8b1c86db55e46d2024-04-02T09:28:41ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0681A study on muti-strategy predator algorithm for passenger traffic prediction with big dataFu Yujie0Gao Ming1Zhu Xiaohui2Fu Jihong31Department of Geography, University College London, London, WC1E 6BT, UK.2School of Sport, Exercise and Health Sciences, Loughborough University, Leicestershire, LE11 3TU, UK.3Tourism and Culture Industry Research Institute, Yunnan University of Finance and Economics, Kunming, Yunnan, 650221, China.4Yunnan Tourism College, Kunming, Yunnan, 650221, China.In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.https://doi.org/10.2478/amns-2024-0681big datapredator algorithmpassenger flow predictionextreme learning machine68t01 |
| spellingShingle | Fu Yujie Gao Ming Zhu Xiaohui Fu Jihong A study on muti-strategy predator algorithm for passenger traffic prediction with big data Applied Mathematics and Nonlinear Sciences big data predator algorithm passenger flow prediction extreme learning machine 68t01 |
| title | A study on muti-strategy predator algorithm for passenger traffic prediction with big data |
| title_full | A study on muti-strategy predator algorithm for passenger traffic prediction with big data |
| title_fullStr | A study on muti-strategy predator algorithm for passenger traffic prediction with big data |
| title_full_unstemmed | A study on muti-strategy predator algorithm for passenger traffic prediction with big data |
| title_short | A study on muti-strategy predator algorithm for passenger traffic prediction with big data |
| title_sort | study on muti strategy predator algorithm for passenger traffic prediction with big data |
| topic | big data predator algorithm passenger flow prediction extreme learning machine 68t01 |
| url | https://doi.org/10.2478/amns-2024-0681 |
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