Using Novel Meta-Heuristic Algorithms for Single-Objective Operation of Reservoir Amirkabir

Document Type : Research Article


1 Department of Civil Engineering, Kish International Branch, Islamic Azad University, Kish Island, Iran.

2 Department of Water Science and Engineering, Shahre-e-qods Branch, Islamic Azad University, Tehran, Iran.

3 Department of Water Science and Engineering, University of Birjand, Birjand, Iran.

4 Department of Civil Engineering, University of Birjand, Birjand, Iran.


 In this study, the objective function of minimizing the total power of the difference between the demand of agriculture and release has been used to solve the problem of optimizing the operation of the Amirkabir reservoir. The purpose of this study was to evaluate the performance of single-objective versions of algorithms such as multi-verse optimizer and genetic algorithm, as well as the performance of a combination of these two algorithms (MVGA). The results of the study of meta-heuristic algorithms indicated that among the multi-verse, genetic algorithm and MVGA algorithm, the MVGA algorithm similar to GA, has a lower number of iterations with objective function values of 24.29 and 24.22, respectively, better than the MVO algorithm with objective function values 29.14. The results of this study showed that to increase the efficiency of one algorithm, it can be combined with another algorithm. In this study, the combination of a genetic algorithm with multi-world algorithm has improved the performance of the multi-world algorithm by 16.64%.


Main Subjects

[1] M. Zeynali, O. Mohamad Reza Pour, F. Frooghi, Using Firefly Algorithm for Optimizing Operation of Doroudzan Reservoir, Irrigation and Water Engineering, 6(1) (2015) 33-45.
[2] O. Mohammadrezapour, M. Zeynali, Comparison of meta-heuristic algorithms in the optimal operation of multi-reservoir (a case study: Golestan and Voshmgir dams), Journal of Water and Soil Science, 22(1) (2018).
[3] O.M.R. Pour, M.J. Zeynali, Application of an max-min ant system algorithm for optimal operation of multi-reservoirs (case study: Golestan and Voshmgir reservoir dams), International Journal of Agriculture and Crop Sciences (IJACS), 8(1) (2015) 27-33.
[4] Y.H. Al-Aqeeli, O.M.M. Agha, Optimal operation of multi-reservoir system for hydropower production using particle swarm optimization algorithm, Water Resources Management, 34(10) (2020) 3099-3112.
[5] X. Zeng, T. Hu, X. Cai, Y. Zhou, X. Wang, Improved dynamic programming for parallel reservoir system operation optimization, Advances in Water Resources, 131 (2019) 103373.
[6] D. Rani, M. Pant, S. Jain, Dynamic programming integrated particle swarm optimization algorithm for reservoir operation, International Journal of System Assurance Engineering and Management, 11(2) (2020) 515-529.
[7] M. Rabiei, M. Aalami, S. Talatahari, Reservoir operation optimization using CBO, ECBO and VPS algorithms, Iran University of Science & Technology, 8(3) (2018) 489-509.
[8] A. Moridi, J. Yazdi, Optimal allocation of flood control capacity for multi-reservoir systems using multi-objective optimization approach, Water Resources Management, 31(14) (2017) 4521-4538.
[9] J. Anand, A. Gosain, R. Khosa, Optimisation of multipurpose reservoir operation by coupling SWAT and genetic algorithm for optimal operating policy (case study: Ganga River basin),  (2018).
[10] K. Srinivasan, K. Kumar, Multi-objective simulation-optimization model for long-term reservoir operation using piecewise linear hedging rule, Water resources management, 32(5) (2018) 1901-1911.
[11] Z.-k. Feng, S. Liu, W.-j. Niu, B.-j. Li, W.-c. Wang, B. Luo, S.-m. Miao, A modified sine cosine algorithm for accurate global optimization of numerical functions and multiple hydropower reservoirs operation, Knowledge-Based Systems, 208 (2020) 106461.
[12] Z.-k. Feng, W.-j. Niu, S. Liu, B. Luo, S.-m. Miao, K. Liu, Multiple hydropower reservoirs operation optimization by adaptive mutation sine cosine algorithm based on neighborhood search and simplex search strategies, Journal of Hydrology, 590 (2020) 125223.
[13] Z. Zhang, H. Qin, L. Yao, Y. Liu, Z. Jiang, Z. Feng, S. Ouyang, Improved Multi-objective Moth-flame Optimization Algorithm Based on R-domination for cascade reservoirs operation, Journal of Hydrology, 581 (2020) 124431.
[14] Z.M. Yaseen, M.F. Allawi, H. Karami, M. Ehteram, S. Farzin, A.N. Ahmed, S.B. Koting, N.S. Mohd, W.Z.B. Jaafar, H.A. Afan, A hybrid bat–swarm algorithm for optimizing dam and reservoir operation, Neural Computing and Applications, 31(12) (2019) 8807-8821.
[15] Y. Xia, Z.-k. Feng, W.-j. Niu, H. Qin, Z.-q. Jiang, J.-z. Zhou, Simplex quantum-behaved particle swarm optimization algorithm with application to ecological operation of cascade hydropower reservoirs, Applied Soft Computing, 84 (2019) 105715.
[16] M. Zeynali, R.P.O. MOHAMAD, F. FROOGHI, Comparison of imperialist competitive algorithm (ICA) and ant colony algorithm (ACO) for optimizing exploitation of Doroudzan reservoir with application of chain constraints approach,  (2016).
[17] W.-j. Niu, Z.-k. Feng, C.-t. Cheng, X.-y. Wu, A parallel multi-objective particle swarm optimization for cascade hydropower reservoir operation in southwest China, Applied Soft Computing, 70 (2018) 562-575.
[18] M.H. Afshar, R. Hajiabadi, A novel parallel cellular automata algorithm for multi-objective reservoir operation optimization, Water resources management, 32(2) (2018) 785-803.
[19] Z.-k. Feng, W.-j. Niu, C.-t. Cheng, Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm, Energy, 153 (2018) 706-718.
[20] A. Azari, S. Hamzeh, S. Naderi, Multi-objective optimization of the reservoir system operation by using the hedging policy, Water resources management, 32(6) (2018) 2061-2078.
[21] e. f, o. b, s. a, Optimal Operation of the Conjunctive Aquifers - Dam system: The Genetic Programming Approach, Water Resources Engineering, 7(21) (2014) 51-66.
[22] S. Mirjalili, S.M. Mirjalili, A. Hatamlou, Multi-verse optimizer: a nature-inspired algorithm for global optimization, Neural Computing and Applications, 27(2) (2016) 495-513.