Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

• Published in: Sustainability 2019-04, Vol.11 (8), p.2337
• Main Authors: Valikhan-Anaraki, Mahdi, Mousavi, Sayed-Farhad, Farzin, Saeed, Karami, Hojat, Ehteram, Mohammad, Kisi, Ozgur, Fai, Chow Ming, Hossain, Md. Shabbir, Hayder, Gasim, Ahmed, Ali Najah, El-Shafie, Amr H., Bin Hashim, Huzaifa, Afan, Haitham Abdulmohsin, Lai, Sai Hin, El-Shafie, Ahmed
• Format: Article
• Language: English
• Subjects: Algorithms; Biogeography; Climate change; Computer applications; Computing time; Convergence; Dams; Decision making; Downstream; Downstream effects; Evolutionary algorithms; Genetic algorithms; Heuristic; Hydroelectric power; Irrigation; Moisture content; Multiple criterion; Objective function; Optimization; Optimization algorithms; Power plants; Reservoirs; Resource management; Root-mean-square errors; Sustainability; Water content; Water management; Water resources; Water resources management
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su11082337

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.