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System performances analysis of reservoir optimization�simulation model in application of artificial bee colony algorithm

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dc.citedby10
dc.contributor.authorHossain M.S.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorMahzabin M.S.en_US
dc.contributor.authorZawawi M.H.en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid56584655200en_US
dc.contributor.authorid39162217600en_US
dc.date.accessioned2023-05-29T06:51:02Z
dc.date.available2023-05-29T06:51:02Z
dc.date.issued2018
dc.descriptionDecision making; Evolutionary algorithms; Genetic algorithms; Optimization; Particle swarm optimization (PSO); Reservoirs (water); Stochastic models; Stochastic systems; Artificial bee colonies (ABC); Artificial bee colony algorithms; Optimization techniques; Performance checking indices; Performances analysis; Reservoir optimizations; Reservoir release; Stochastic dynamic programming; Dynamic programmingen_US
dc.description.abstractIn reservoir system operation, optimization is very much essential and the compatibility of different optimization techniques is essential to be checked by some performance checking indices. In this study, various types of performance-measuring index are used and compared to provide a complete knowledge on adopting different approaches. Here, the considered performance-measuring indicators will check the operation policy in terms of three different scenarios�how the method is efficient in achieving best results (reliability); how vulnerable the method is for different critical situation (vulnerability); and how capable it is to handle a failure of the model (resiliency). Therefore, the study proposed the artificial bee colony (ABC) optimization technique to develop an optimal water release policy for the well-known Aswan High Dam, Egypt. Particle swarm optimization, genetic algorithm and neural network-based stochastic dynamic programming are also used in a view of comparing model performances. A release curve is developed for every month as a guidance to the decision maker. Simulation has been done for each method using historical actual inflow data, and reliability, resiliency and vulnerability are measured. All model indicators proved that the release policy provided by ABC optimization outperforms in terms of achieving minimum water deficit, less waste of water and handling critical situations. � 2016, The Natural Computing Applications Forum.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00521-016-2798-2
dc.identifier.epage2112
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85007429410
dc.identifier.spage2101
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85007429410&doi=10.1007%2fs00521-016-2798-2&partnerID=40&md5=c356f233449198562e8b48f8dcdefecb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23689
dc.identifier.volume30
dc.publisherSpringer Londonen_US
dc.sourceScopus
dc.sourcetitleNeural Computing and Applications
dc.titleSystem performances analysis of reservoir optimization�simulation model in application of artificial bee colony algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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