Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation

Abstract Deriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimiz...

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Autores principales:	Mohammad Reza Sharifi, Saeid Akbarifard, Kourosh Qaderi, Mohamad Reza Madadi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b4a7c21afd694acfb593b4038e7824ba
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spelling	oai:doaj.org-article:b4a7c21afd694acfb593b4038e7824ba2021-12-02T14:53:48ZComparative analysis of some evolutionary-based models in optimization of dam reservoirs operation10.1038/s41598-021-95159-42045-2322https://doaj.org/article/b4a7c21afd694acfb593b4038e7824ba2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95159-4https://doaj.org/toc/2045-2322Abstract Deriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimization algorithm (HHO), seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), tunicate swarm algorithm (TSA) and moth swarm algorithm (MSA) were employed, for the first time, to optimal operation of Halilrood multi-reservoir system. This system includes three dams with parallel and series arrangements simultaneously. The results of mentioned algorithms were compared with two well-known methods of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The objective function of the optimization model was defined as the minimization of total deficit over 223 months of reservoirs operation. Four performance criteria of reliability, resilience, vulnerability and sustainability were used to compare the algorithms’ efficiency in optimization of this multi-reservoir operation. It was observed that the MSA algorithm with the best value of objective function (6.96), the shortest CPU run-time (6738 s) and the fastest convergence rate (< 2000 iterations) was the superior algorithm, and the HHO algorithm placed in the next rank. The GA, and the PSO were placed in the middle ranks and the SOA, and the STOA placed in the lowest ranks. Furthermore, the comparison of utilized algorithms in terms of sustainability index indicated the higher performance of the MSA in generating the best operation scenarios for the Halilrood multi-reservoir system. The application of robust EAs, notably the MSA algorithm, to improve the operation policies of multi-reservoir systems is strongly recommended to water resources managers and decision-makers.Mohammad Reza SharifiSaeid AkbarifardKourosh QaderiMohamad Reza MadadiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad Reza Sharifi Saeid Akbarifard Kourosh Qaderi Mohamad Reza Madadi Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation
description	Abstract Deriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimization algorithm (HHO), seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), tunicate swarm algorithm (TSA) and moth swarm algorithm (MSA) were employed, for the first time, to optimal operation of Halilrood multi-reservoir system. This system includes three dams with parallel and series arrangements simultaneously. The results of mentioned algorithms were compared with two well-known methods of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The objective function of the optimization model was defined as the minimization of total deficit over 223 months of reservoirs operation. Four performance criteria of reliability, resilience, vulnerability and sustainability were used to compare the algorithms’ efficiency in optimization of this multi-reservoir operation. It was observed that the MSA algorithm with the best value of objective function (6.96), the shortest CPU run-time (6738 s) and the fastest convergence rate (< 2000 iterations) was the superior algorithm, and the HHO algorithm placed in the next rank. The GA, and the PSO were placed in the middle ranks and the SOA, and the STOA placed in the lowest ranks. Furthermore, the comparison of utilized algorithms in terms of sustainability index indicated the higher performance of the MSA in generating the best operation scenarios for the Halilrood multi-reservoir system. The application of robust EAs, notably the MSA algorithm, to improve the operation policies of multi-reservoir systems is strongly recommended to water resources managers and decision-makers.
format	article
author	Mohammad Reza Sharifi Saeid Akbarifard Kourosh Qaderi Mohamad Reza Madadi
author_facet	Mohammad Reza Sharifi Saeid Akbarifard Kourosh Qaderi Mohamad Reza Madadi
author_sort	Mohammad Reza Sharifi
title	Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation
title_short	Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation
title_full	Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation
title_fullStr	Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation
title_full_unstemmed	Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation
title_sort	comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b4a7c21afd694acfb593b4038e7824ba
work_keys_str_mv	AT mohammadrezasharifi comparativeanalysisofsomeevolutionarybasedmodelsinoptimizationofdamreservoirsoperation AT saeidakbarifard comparativeanalysisofsomeevolutionarybasedmodelsinoptimizationofdamreservoirsoperation AT kouroshqaderi comparativeanalysisofsomeevolutionarybasedmodelsinoptimizationofdamreservoirsoperation AT mohamadrezamadadi comparativeanalysisofsomeevolutionarybasedmodelsinoptimizationofdamreservoirsoperation
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Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation

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