A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction
In any economy, it is essential to monitor the rate of population change closely. Governments employ various strategies and programs to regulate population growth since different population growth rates have distinct economic consequences. This paper reveals a global trend of reduced desire to have...
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| Format: | Article |
| Language: | English |
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REA Press
2023-12-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_186876_d517302e1cb4ebd97feed17659483c28.pdf |
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| author | Milad Shahvaroughi Farahani Hamed Farrokhi-Asl Farrokhi-Asl Ghazal Ghasemi |
| author_facet | Milad Shahvaroughi Farahani Hamed Farrokhi-Asl Farrokhi-Asl Ghazal Ghasemi |
| author_sort | Milad Shahvaroughi Farahani |
| collection | DOAJ |
| description | In any economy, it is essential to monitor the rate of population change closely. Governments employ various strategies and programs to regulate population growth since different population growth rates have distinct economic consequences. This paper reveals a global trend of reduced desire to have children, with variations across countries. The paper aims to predict the population growth rate in England by employing Artificial Neural Networks (ANN) in combination with various meta-heuristic algorithms, including the Sparrow Search Algorithm (SSA). The selection of SSA and other algorithms is based on factors such as accuracy and computational efficiency. A set of 18 economic indicators serves as input variables, and a Genetic Algorithm (GA) is used for feature selection. The data used for analysis spans the most recent ten years and is presented on a monthly basis. The results indicate that SSA exhibits the lowest prediction errors for the population growth rate among the applied algorithms in this paper. The primary contribution of this study lies in the application of hybrid algorithms that combine SSA-ANN with other algorithms, such as LA. The paper also emphasizes the inclusion of influential and impactful indices as input variables to enhance prediction accuracy. |
| format | Article |
| id | doaj-art-cab2663ecab54db7b9feb8cfc90f7226 |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2023-12-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-cab2663ecab54db7b9feb8cfc90f72262025-01-30T12:23:08ZengREA PressBig Data and Computing Visions2783-49562821-014X2023-12-013416018510.22105/bdcv.2024.426714.1170186876A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate predictionMilad Shahvaroughi Farahani0Hamed Farrokhi-Asl Farrokhi-Asl1Ghazal Ghasemi2Department of Finance, Khatam University, Tehran, Iran.Sheldon B. Lubar Business School, University of Wisconsin Milwaukee, Wisconsin, USA.Department of Law, Islamic Azad University, Tehran, Iran.In any economy, it is essential to monitor the rate of population change closely. Governments employ various strategies and programs to regulate population growth since different population growth rates have distinct economic consequences. This paper reveals a global trend of reduced desire to have children, with variations across countries. The paper aims to predict the population growth rate in England by employing Artificial Neural Networks (ANN) in combination with various meta-heuristic algorithms, including the Sparrow Search Algorithm (SSA). The selection of SSA and other algorithms is based on factors such as accuracy and computational efficiency. A set of 18 economic indicators serves as input variables, and a Genetic Algorithm (GA) is used for feature selection. The data used for analysis spans the most recent ten years and is presented on a monthly basis. The results indicate that SSA exhibits the lowest prediction errors for the population growth rate among the applied algorithms in this paper. The primary contribution of this study lies in the application of hybrid algorithms that combine SSA-ANN with other algorithms, such as LA. The paper also emphasizes the inclusion of influential and impactful indices as input variables to enhance prediction accuracy.https://www.bidacv.com/article_186876_d517302e1cb4ebd97feed17659483c28.pdfartificial neural networkmeta-heuristic algorithmssparrow search algorithmmayfly algorithmlichtenberg algorithmpopulation growth rate |
| spellingShingle | Milad Shahvaroughi Farahani Hamed Farrokhi-Asl Farrokhi-Asl Ghazal Ghasemi A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction Big Data and Computing Visions artificial neural network meta-heuristic algorithms sparrow search algorithm mayfly algorithm lichtenberg algorithm population growth rate |
| title | A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction |
| title_full | A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction |
| title_fullStr | A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction |
| title_full_unstemmed | A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction |
| title_short | A sparrow search algorithm based hybrid meta-heuristic algorithm for population growth rate prediction |
| title_sort | sparrow search algorithm based hybrid meta heuristic algorithm for population growth rate prediction |
| topic | artificial neural network meta-heuristic algorithms sparrow search algorithm mayfly algorithm lichtenberg algorithm population growth rate |
| url | https://www.bidacv.com/article_186876_d517302e1cb4ebd97feed17659483c28.pdf |
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