An Imperialist Competitive Algorithm (ICA)-Based Approach to Optimize the Reservoir Storage of the Kahir Dam

International Journal of Agricultural Management and Development

مقاله 7، دوره 10، شماره 2، شهریور 2020، صفحه 207-218   

نوع مقاله: Research Article

نویسندگان

Ali Sardar Shahraki^{* 1}؛ Somayeh Emami²

¹Assistant Professor of Agricultural Economics, University of Sistan and Baluchestan, Iran

²Ph.D. Candidate of Water Engineering, Tabriz University, Iran

چکیده

Water scarcity, especially in Iran and during the recent droughts, emphasizes the importance of achieving an optimal operation policy for large dam reservoirs. In the last two decades, the annual optimization of dam reservoirs under controlled conditions, as well as climatic and real conditions, has attracted many researchers and experts. This study proposes a new approach to predict reservoir dam storage. The imperialist competitive algorithm (ICA) is a new approach in the field of evolutionary computation that calculates an optimal solution for different optimization problems. Using mathematical modeling of the social-psychological evolution process, ICA provides a new approach to solve mathematical optimization problems, and compared to other algorithms, it has appropriate speed and high convergence rate in finding an optimal answer. This research used the ICA for the annual optimization of the Kahir reservoir to derive optimal policies. Objective function downstream water issue needs to establish relationships based on continuity were selected. Comparison of ICA model in population 100 showed that the ICA algorithm with average best objective function value of 125, 114.6, and 85.60 with a number of further evaluations of the objective function to achieve higher capacity is the optimum answer. The results showed a 6.1 percent error in the implementation of the ICA algorithm between the observed and predicted storages. The results of applying the ICA to the annual optimization problem demonstrate the capability of the proposed method.

کلیدواژه‌ها

Optimal operation؛ imperialist competitive algorithm؛ prediction of reservoir dam storage؛ Kahir Dam

مراجع

Afshar, A., M.J., Emami, & Masoumi, F. (2014). Optimizing water supply and hydropower reservoir operation rule curves: An imperialist competitive algorithm approach. Engineering Optimization, 46(10), 170-181.

Afshar, M.H. (2003). Extension of the constrained particle swarm optimization algorithm to optimal operation of multi-reservoirs system. International Journal of Electrical and Energy Systems, 51, 71-81.

Arjoon, D., Tilmant, A., & Herrmann, M. (2016). Sharing water and benefits in trans boundary river basins, Hydrol. Hydrology and Earth System Sciences, 21, 1-25.

Asl Rousta, B., Araghinejad, S. (2015). Development of a multi criteria decision making tool for a water resources decision support system. Water Resources Management, 29(15), 5713-5727.

Habibi Davijani, M., Banihabib, M.E., Nadjafzadeh Anvar, A., & Hashemi S.R. (2016). Optimization model for the allocation of water resources based on the maximization of employment in the agriculture and industry sectors. Journal of Hydrology, 533, 430-438.

Iranian Ministry of Energy. (2014). Reports from the Office of Basic Water Resources Studies, Tehran, Iran.

Karamouz, M., Nazif, S., Sherafat, M.A., & Zahmatkesh, Z. (2014). Development of an optimal reservoir operation scheme using extended evolutionary computing algorithms based on conflict resolution approach: A case study. Water Resources Management, 28, 3539-54.

Mianabadi, H., Mostert, E., Saket Pande, S., & Giesen, N. (2015). Weighted bankruptcy rules and transboundary water resources allocation. Water Resource Management, 29, 2303–2321.

Mourad, K.A., & Alshihabi, O. (2016). Assessment of future Syrian water resources supply and demand by the WEAP model. Hydrological Sciences Journal, 61(2), 393-401.

Rafiee Anzab, N., Jamshid Mousavi S., Bentolhoda A, Rousta., Joong Hoon K. (2016). Simulation Optimization for Optimal Sizing of Water Transfer Systems. Harmony Search Algorithm, 382, 365-375

Reports of Iran Meteorological Organization (2017). National Climate Research Center. Tehran, Iran.

Safari, N., Zarghami, M., & Szidarovszky, F. (2014). Nash bargaining and leader–follower models in water allocation: Application to the Zarrinehrud River basin, Iran. Applied Mathematical Modeling, 38(7–8), 1959–1968.

Sardar Shahraki, A. (2016). Optimal allocation of water sources in the Hirmand catchment by using game theory and evaluation of management scenarios. Unpublished dissertation in agricultural economics, University of Sistan and Baluchestan, Zahedan.

Sardar Shahraki, A., Shahraki, J., & Hashemi Monfared, S.A. (2016). Ranking and Level of Development According to the Agricultural Indices, Case Study: Sistan Region. International Journal of Agricultural Management and Development, 6(1), 93-100.

Sardar Shahraki, A., Shahraki, J., & Hashemi Monfared, S.A. (2016). An Application of WEAP Model in Water Resources Management Considering the Environmental Scenarios and Economic Assessment Case Study: Hirmand Catchment. Modern Applied Science, 10(5), 49-56.

Sardar Shahraki, A., Shahraki, J., & Hashemi Monfared, S.A. (2018). An Integrated Fuzzy Multi-Criteria Decision-Making Method Combined with the WEAP Model for Prioritizing Agricultural Development, Case Study: Hirmand Catchment. ECOPERSIA, 6(4), 205-214.

Shahraki J., & AliAhmadi, N. (2014). Economic analysis of water demand in greenhouses of Khash Township. International Journal of Agricultural Management and Development, 4(2), 87-93.

Shahraki, J., Yaghoubi, M., Sardar Shahraki A., Esfandiari, M. (2012). A survey on the level of mechanization development in Sistan and Baluchestan, Iran. Journal of Applied Sciences Research, 8, 2267-2271.

Yang X. Sh. (2009). Firefly algorithm for multi-model optimization. Stochastic Algorithm. Foundations and Applications, 5792(12), 169-178.

Zhang, Zh., Jiang, Y., Zhang, Sh., Geng, S., Wang, H., & Sang, G. (2014). An adaptive particle swarm optimization algorithm for reservoir operation optimization. Applied Soft Computing, 18, 167-77.