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Optimization of Reservoir Operation using New Hybrid Algorithm

Due to the scarcity of fresh water resources, exploiting dams’ reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers’ water needs with high reliability. In this research, a new hybrid approach of Artificial Fish Swarm Algo...

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Main Authors: Yaseen, Zaher Mundher, Karami, Hojat, Ehteram, Mohammad, Mohd, Nuruol Syuhadaa, Mousavi, Sayed Farhad, Lai, Sai Hin, Kisi, Ozgur, Farzin, Saeed, Kim, Sungwon, El-Shafie, Ahmed
Format: Article
Published: Springer Verlag (Germany) 2018
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/20304/
https://doi.org/10.1007/s12205-018-2095-y
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spelling my.um.eprints.203042019-02-14T04:03:18Z http://eprints.um.edu.my/20304/ Optimization of Reservoir Operation using New Hybrid Algorithm Yaseen, Zaher Mundher Karami, Hojat Ehteram, Mohammad Mohd, Nuruol Syuhadaa Mousavi, Sayed Farhad Lai, Sai Hin Kisi, Ozgur Farzin, Saeed Kim, Sungwon El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) Due to the scarcity of fresh water resources, exploiting dams’ reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers’ water needs with high reliability. In this research, a new hybrid approach of Artificial Fish Swarm Algorithm (AFSA) and Particle Swarm Optimization Algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid Algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant’s objective function. Springer Verlag (Germany) 2018 Article PeerReviewed Yaseen, Zaher Mundher and Karami, Hojat and Ehteram, Mohammad and Mohd, Nuruol Syuhadaa and Mousavi, Sayed Farhad and Lai, Sai Hin and Kisi, Ozgur and Farzin, Saeed and Kim, Sungwon and El-Shafie, Ahmed (2018) Optimization of Reservoir Operation using New Hybrid Algorithm. KSCE Journal of Civil Engineering, 22 (11). pp. 4668-4680. ISSN 1226-7988 https://doi.org/10.1007/s12205-018-2095-y doi:10.1007/s12205-018-2095-y
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Yaseen, Zaher Mundher
Karami, Hojat
Ehteram, Mohammad
Mohd, Nuruol Syuhadaa
Mousavi, Sayed Farhad
Lai, Sai Hin
Kisi, Ozgur
Farzin, Saeed
Kim, Sungwon
El-Shafie, Ahmed
Optimization of Reservoir Operation using New Hybrid Algorithm
description Due to the scarcity of fresh water resources, exploiting dams’ reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers’ water needs with high reliability. In this research, a new hybrid approach of Artificial Fish Swarm Algorithm (AFSA) and Particle Swarm Optimization Algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid Algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant’s objective function.
format Article
author Yaseen, Zaher Mundher
Karami, Hojat
Ehteram, Mohammad
Mohd, Nuruol Syuhadaa
Mousavi, Sayed Farhad
Lai, Sai Hin
Kisi, Ozgur
Farzin, Saeed
Kim, Sungwon
El-Shafie, Ahmed
author_facet Yaseen, Zaher Mundher
Karami, Hojat
Ehteram, Mohammad
Mohd, Nuruol Syuhadaa
Mousavi, Sayed Farhad
Lai, Sai Hin
Kisi, Ozgur
Farzin, Saeed
Kim, Sungwon
El-Shafie, Ahmed
author_sort Yaseen, Zaher Mundher
title Optimization of Reservoir Operation using New Hybrid Algorithm
title_short Optimization of Reservoir Operation using New Hybrid Algorithm
title_full Optimization of Reservoir Operation using New Hybrid Algorithm
title_fullStr Optimization of Reservoir Operation using New Hybrid Algorithm
title_full_unstemmed Optimization of Reservoir Operation using New Hybrid Algorithm
title_sort optimization of reservoir operation using new hybrid algorithm
publisher Springer Verlag (Germany)
publishDate 2018
url http://eprints.um.edu.my/20304/
https://doi.org/10.1007/s12205-018-2095-y
_version_ 1643691239911981056
score 13.211869

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