Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies

One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (P...

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Main Authors: Valikhan-Anaraki, M., Mousavi, S.-F., Farzin, S., Karami, H., Ehteram, M., Kisi, O., Fai, C.M., Hossain, M.S., Hayder, G., Ahmed, A.N., El-Shafie, A.H., Bin Hashim, H., Afan, H.A., Lai, S.H., El-Shafie, A.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-130992020-08-18T02:54:15Z Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies Valikhan-Anaraki, M. Mousavi, S.-F. Farzin, S. Karami, H. Ehteram, M. Kisi, O. Fai, C.M. Hossain, M.S. Hayder, G. Ahmed, A.N. El-Shafie, A.H. Bin Hashim, H. Afan, H.A. Lai, S.H. El-Shafie, A. One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991-2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands. © 2019 by the authors. 2020-02-03T03:30:23Z 2020-02-03T03:30:23Z 2019-11 Article 10.3390/su11082337 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991-2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands. © 2019 by the authors.
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author Valikhan-Anaraki, M.
Mousavi, S.-F.
Farzin, S.
Karami, H.
Ehteram, M.
Kisi, O.
Fai, C.M.
Hossain, M.S.
Hayder, G.
Ahmed, A.N.
El-Shafie, A.H.
Bin Hashim, H.
Afan, H.A.
Lai, S.H.
El-Shafie, A.
spellingShingle Valikhan-Anaraki, M.
Mousavi, S.-F.
Farzin, S.
Karami, H.
Ehteram, M.
Kisi, O.
Fai, C.M.
Hossain, M.S.
Hayder, G.
Ahmed, A.N.
El-Shafie, A.H.
Bin Hashim, H.
Afan, H.A.
Lai, S.H.
El-Shafie, A.
Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
author_facet Valikhan-Anaraki, M.
Mousavi, S.-F.
Farzin, S.
Karami, H.
Ehteram, M.
Kisi, O.
Fai, C.M.
Hossain, M.S.
Hayder, G.
Ahmed, A.N.
El-Shafie, A.H.
Bin Hashim, H.
Afan, H.A.
Lai, S.H.
El-Shafie, A.
author_sort Valikhan-Anaraki, M.
title Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
title_short Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
title_full Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
title_fullStr Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
title_full_unstemmed Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
title_sort development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
publishDate 2020
_version_ 1678595892475592704
score 13.214268