Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

Valikhan-Anaraki, Mahdi and Mousavi, Sayed-Farhad and Farzin, Saeed and Karami, Hojat and Ehteram, Mohammad and Kisi, Ozgur and Fai, Chow Ming and Hossain, Md Shabbir and Hayder, Gasim and Ahmed, Ali Najah and El-Shafie, Amr H. and Hashim, Huzaifa and Afan, Haitham Abdulmohsin and Lai, Sai Hin and El-Shafie, Ahmed (2019) Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies. Sustainability, 11 (8). p. 2337. ISSN 2071-1050, DOI

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One 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.

Item Type: Article
Funders: Universiti Tenaga Nasional (UNITEN) under Bold Grant, University of Malaya Research Grant (UMRG) coded RP025A-18SUS University of Malaya, Malaysia
Uncontrolled Keywords: hybrid algorithm; particle swarm optimization algorithm; bat algorithm; water resources management
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 Dec 2019 08:17
Last Modified: 28 Feb 2020 01:41

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