Forecasting Model for the Number of Breeding Sows Based on Pig’s Months of Age Transfer and Improved Flower Pollination Algorithm-Back Propagation Neural Network
Regulating the number of breeding sows (NBS) is crucial for pork supply–demand balance. Current forecasting methods for NBS fail to consider the principle of pig’s months of age (MOA) transfer and the impact of factors like diseases and policies on NBS fluctuations, leading to unsatisfactory accurac...
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| Published in: | Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 7; pp. 5826 - 5858 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.04.2024
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | Regulating the number of breeding sows (NBS) is crucial for pork supply–demand balance. Current forecasting methods for NBS fail to consider the principle of pig’s months of age (MOA) transfer and the impact of factors like diseases and policies on NBS fluctuations, leading to unsatisfactory accuracy. To bridge the research gap, a two-part forecasting model for the NBS was developed. In the first part, a recurrence forecasting model was established according to the growth characteristics of pigs and the principle of pig’s MOA transfer. In the second part, the random disturbance term was introduced to consider the influence of plague, policy and other factors on the NBS, and a forecasting method for random disturbance term based on Improved Flower Pollination Algorithm-Back Propagation Neural Network (IFPA-BPNN) was given. Subsequently, the proposed IFA and other newer optimization algorithms were evaluated on CEC 2017 test suite to verify the effectiveness and superiority of IFA. Lastly, the proposed model was employed to forecast the NBS in Heilongjiang Province and Anhui Province of China from 2009 to 2021. Compared to other time series forecasting models, the proposed model showed superior accuracy, confirming its scientific and effective nature. Relevant managerial insights were provided at the end of this paper. |
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| AbstractList | Regulating the number of breeding sows (NBS) is crucial for pork supply–demand balance. Current forecasting methods for NBS fail to consider the principle of pig’s months of age (MOA) transfer and the impact of factors like diseases and policies on NBS fluctuations, leading to unsatisfactory accuracy. To bridge the research gap, a two-part forecasting model for the NBS was developed. In the first part, a recurrence forecasting model was established according to the growth characteristics of pigs and the principle of pig’s MOA transfer. In the second part, the random disturbance term was introduced to consider the influence of plague, policy and other factors on the NBS, and a forecasting method for random disturbance term based on Improved Flower Pollination Algorithm-Back Propagation Neural Network (IFPA-BPNN) was given. Subsequently, the proposed IFA and other newer optimization algorithms were evaluated on CEC 2017 test suite to verify the effectiveness and superiority of IFA. Lastly, the proposed model was employed to forecast the NBS in Heilongjiang Province and Anhui Province of China from 2009 to 2021. Compared to other time series forecasting models, the proposed model showed superior accuracy, confirming its scientific and effective nature. Relevant managerial insights were provided at the end of this paper. |
| Author | Yang, Jingnan Wang, Jiquan Song, Haohao Zhang, Hongyu |
| Author_xml | – sequence: 1 givenname: Haohao surname: Song fullname: Song, Haohao organization: College of Engineering, Northeast Agricultural University – sequence: 2 givenname: Hongyu surname: Zhang fullname: Zhang, Hongyu organization: College of Engineering, Northeast Agricultural University – sequence: 3 givenname: Jingnan surname: Yang fullname: Yang, Jingnan organization: College of Engineering, Northeast Agricultural University – sequence: 4 givenname: Jiquan orcidid: 0000-0002-1498-2602 surname: Wang fullname: Wang, Jiquan email: wang-jiquan@163.com organization: College of Engineering, Northeast Agricultural University |
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| CitedBy_id | crossref_primary_10_3390_su16166907 crossref_primary_10_3390_agriculture14091592 crossref_primary_10_3390_agriculture15141484 |
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| Keywords | IFPA-BPNN Pork price Forecasting The principle of pig’s MOA transfer The number of breeding sows |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Artificial neural networks Back propagation Back propagation networks Computer Science Forecasting Hogs Machines Manufacturing Mechanical Engineering Neural networks Principles Processes Swine |
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| Title | Forecasting Model for the Number of Breeding Sows Based on Pig’s Months of Age Transfer and Improved Flower Pollination Algorithm-Back Propagation Neural Network |
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