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Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies

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dc.citedby18
dc.contributor.authorValikhan-Anaraki M.en_US
dc.contributor.authorMousavi S.-F.en_US
dc.contributor.authorFarzin S.en_US
dc.contributor.authorKarami H.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorHossain M.S.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.H.en_US
dc.contributor.authorBin Hashim H.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57209237974en_US
dc.contributor.authorid7003344568en_US
dc.contributor.authorid55315758000en_US
dc.contributor.authorid36863982200en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57207789882en_US
dc.contributor.authorid56800153400en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:26:06Z
dc.date.available2023-05-29T07:26:06Z
dc.date.issued2019
dc.descriptioncomputer system; dam; genetic algorithm; irrigation system; optimization; particle size; water management; Chondrichthyesen_US
dc.description.abstractOne 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2337
dc.identifier.doi10.3390/su11082337
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85066942200
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066942200&doi=10.3390%2fsu11082337&partnerID=40&md5=667a404a1d226b4403320de9119c0fc6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24708
dc.identifier.volume11
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleDevelopment of a novel hybrid optimization algorithm for minimizing irrigation deficienciesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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