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Investigating dam reservoir operation optimization using metaheuristic algorithms

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dc.citedby1
dc.contributor.authorLai V.en_US
dc.contributor.authorEssam Y.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57204919704en_US
dc.contributor.authorid57203146903en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:36:00Z
dc.date.available2023-05-29T09:36:00Z
dc.date.issued2022
dc.descriptionDams; Digital storage; Heuristic algorithms; Optimal systems; Reservoir management; Reservoirs (water); Time series analysis; Dam reservoir operation optimization; Dam reservoirs; Harris hawk optimization; Levy flights; Levy-flight whale optimization algorithm; Metaheuristic; Optimisations; Optimization algorithms; Reservoir operation optimizations; Whale optimization algorithm; Optimization; algorithm; dam; hydroelectric power; optimization; power generation; reservoir; water storage; Iranen_US
dc.description.abstractThe optimization of dam reservoir operations is of the utmost importance, as operators strive to maximize revenue while minimizing expenses, risks, and deficiencies. Metaheuristics have recently been investigated extensively by researchers in the management of dam reservoirs. But the animal-concept-based metaheuristic algorithm with L�vy flight integration approach has not been used at Karun-4. This paper investigates the optimization of dam reservoir operation using three unexplored metaheuristics: the whale optimization algorithm (WOA), the Levy-flight WOA (LFWOA), and the Harris hawks optimization algorithm (HHO). Utilizing a time series data set on the hydrological and climatic characteristics of the Karun-4 hydroelectric reservoir in Iran, an analysis was conducted. The objective functions and constraints of the Karun-4 hydropower reservoir were examined throughout the optimization procedure. HHO produces the best optimal value, the least-worst optimal value, the best average optimal value, and the best standard deviation (SD) with scores of 0.000026, 0.001735, 0.000520, and 0.000614, respectively, resulting in the best overall ranking mean (RM) with a score of 1.5 at Karun-4. Throughout the duration of the test, the optimized trends of water release and water storage indicate that HHO is superior to the other investigated metaheuristics. WOA has the best correlation of variation (CV) with a score of 0.090195, while LFWOA has the best convergence rate (3.208�s) and best CPU time. Overall, it can be concluded that HHO has the most desirable performance in terms of optimization. Yet, current studies indicate that both WOA and LFWOA generate positive and comparable outcomes. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo280
dc.identifier.doi10.1007/s13201-022-01794-1
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85141578296
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85141578296&doi=10.1007%2fs13201-022-01794-1&partnerID=40&md5=e2a69bd1c7a23eb2ed84e26854ed8c15
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26642
dc.identifier.volume12
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Water Science
dc.titleInvestigating dam reservoir operation optimization using metaheuristic algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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