Optimal Operation of Dam Reservoir Using Whale Optimization Algorithm and Its Hybrid with Genetic Algorithm Based on Multi-Criteria Decision Making

Document Type : Research Article


1 Ph.D. Student, Faculty of Civil Engineering, Semnan University, Semnan.

2 Professor, Faculty of Civil Engineering, Semnan University, Semnan

3 Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan.


Optimal operation of reservoirs is one of the most important issues in water resources management. In the present study after introducing Whale Optimization Algorithm (WOA), the performance of this algorithm is evaluated separately and in the hybrid with the Genetic Algorithm (hybrid WOA-GA) in the optimal operation problem of Salman Farsi dam reservoir. In the present optimization problem, the objective function is defined as minimizing the total deficit during the operation period. Also, the constraints of the reservoir continuity equation, reservoir storage volume and released volume from the reservoir have been applied to the objective function of the problem. The Performance of proposed algorithms is compared with the performance of the Genetic Algorithm (GA) and Non-Linear Programming (NLP). The performance of models has been evaluated based on Reliability, reversibility, vulnerability and stability criteria. The results of optimal solutions showed that the absolute optimum is equal to 0.181 based on NLP method and using Lingo software and the optimal solutions for the models of the hybrid WOA-GA, GA and WOA with 2.9, 24.2 and 337 percent increase compared to the absolute optimum are ranked first to third respectively. A Multi-Criteria Decision-Making technique (MCDM) has been used to select the best model based on the objective function and evaluation criteria of the models' performance. The results of this technique showed that the performance of the hybrid WOA-Ga model is ranked first, and the models of NLP, GA and WOA are in the next ranks, respectively.


Main Subjects

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Volume 6, Issue 2
July 2019
Pages 281-293
  • Receive Date: 23 October 2018
  • Revise Date: 18 December 2018
  • Accept Date: 18 December 2018
  • First Publish Date: 22 June 2019