Water Resources Management

, Volume 28, Issue 11, pp 3539–3554 | Cite as

Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study

  • Mohammad Karamouz
  • Sara Nazif
  • Mohammad Ali Sherafat
  • Zahra Zahmatkesh


Optimal reservoir operation and water allocation are critical issues in sustainable water resource management due to increasing water demand. Multiplicity of stockholders with different objectives and utilities makes reservoir operation a complicated problem with a variety of constraints and objectives to be considered. In this case, the conflict resolution models can be efficiently used to determine the optimal water allocation scheme considering the utility and relative authority of different stakeholders. In this study, the Nash product is used for formulation of the objective function of a reservoir water allocation model. The Analytic Hierarchy Process (AHP) is used to determine the importance of each stockholder in bargaining for water. The Particle Swarm Optimization algorithm (PSO) and the Imperialism Competitive Algorithm (ICA) are applied to solve the proposed optimization model. System performance indices including reliability, resiliency and vulnerability are used to evaluate the performance of optimization algorithms. The simplest and most often-used reservoir policy (Standard Operating Policy, SOP) is also used in order to evaluate the performance of the proposed models. The proposed model is applied to the Karkheh River-Reservoir system located in south western part of Iran as a case study. Results show the significance of the application of conflict resolution models, such as the Nash theory and proposed optimization algorithms, for water allocation in the regional scale especially in complicated water supply systems.


Water allocation optimization Conflict resolution Nash bargaining theory Particle Swarm Optimization Imperialism Competitive Algorithm 


  1. Afshar MH (2012) Large scale reservoir operation by Constrained Particle Swarm Optimization algorithms. J Hydro Environ Res 6(1):75–87CrossRefGoogle Scholar
  2. Atashpaz Gargari E, Lucas C (2007a) Designing an optimal PID controller using Colonial Competitive Algorithm. First Iranian Joint Congress on Intelligent and Fuzzy Systems, Mashhad, IranGoogle Scholar
  3. Atashpaz Gargari E, Lucas C (2007b) Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation, 4661–4667Google Scholar
  4. Atashpaz Gargari E, Hashemzadeh F, Rajabioun R, Lucas C (2008) Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process. Int J Intell Comput Cybern (IJICC) 1(3):337–355CrossRefGoogle Scholar
  5. Babel MS, Das Gupta A, Nayak DK (2005) A model for optimal allocation of water to competing demands. Water Resour Manag 19(6):693–712CrossRefGoogle Scholar
  6. Biabangard-Oskouyi A, Atashpaz-Gargari E, Soltani N, Lucas C (2009) Application of imperialist competitive algorithm for materials property characterization from sharp indentation test. Int J Eng Simul 11–12Google Scholar
  7. Divakar L, Babel MS, Perret SR, Das Gupta A (2011) Optimal allocation of bulk water supplies to competing use sectors based on economic criterion—an application to the Chao Phraya River Basin, Thailand. J Hydrol 401(1–2):22–35CrossRefGoogle Scholar
  8. Ganji A, Kamgar AK, Khalili D, Sepaskhah AR (2002) The application of conflict resolution fundamentals in irrigation scheduling under uncertainty situation. Proceeding of Water resources management conference, Wessex Institute of Technology, Gran CanariaGoogle Scholar
  9. George B, Malano H, Davidson B, Hellegers P, Bharati L, Massuel S (2011) An integrated hydro-economic modelling framework to evaluate water allocation strategies I: model development. Agric Water Manag 98(5):733–746CrossRefGoogle Scholar
  10. Goodarzi E, Ziaei M, Shokri N (2013) Reservoir operation management by optimization and stochastic simulation. J Water Supply Res Technol (AQUA) IWA Publishing 62(3):138–154. doi: 10.2166/aqua.2013.020
  11. Kapelan ZS, Savic DA, Walters GA (2005) Optimal sampling design methodologies for water distribution model calibration. J Hydraulic Eng ASCE, VA, 131(3):190–200Google Scholar
  12. Karamouz M (2005) Qualitative and Quantitative Planning and Management of Water Allocation with Emphasis on Conflict Resolution. Water Research Council, Iranian Water Resources Management OrganizationGoogle Scholar
  13. Karamouz M, Szidarovszky F, Zahraie B (2003) Water resources systems analysis. Lewis Publishers, Boca RatonGoogle Scholar
  14. Karamouz M, Moridi A, Fayyazi HM (2008) Dealing with conflict over water quality and quantity allocation: a case study. J Sci IranGoogle Scholar
  15. Karamouz M, Ahmadi A, Moridi A (2009) Probabilistic reservoir operation using Bayesian stochastic model and support vector machine. Adv Water Resour 32:1588–1600CrossRefGoogle Scholar
  16. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, IV, Perth, Australia, 1942–1948Google Scholar
  17. Li X (2003) A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Cantu-Paz E et al (eds) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2003), 2723, 37–48Google Scholar
  18. Liu D, Chen X, Lou Z (2010) A model for the optimal allocation of water resources in a saltwater intrusion area: a case study in Pearl River Delta in China. Water Resour Manag 24(1):63–81CrossRefGoogle Scholar
  19. Palmer RN, Ryu J, Jeong S, Kim YO (2002) An application of water conflict resolution in the Kum river basin, Korea. Proceeding of ASCE Environmental and Water Resources Institute Conference, Virginia, 19–22Google Scholar
  20. Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing. ACM, New York, pp 603–607CrossRefGoogle Scholar
  21. Rajabioun R, Hashemzadeh F, Atashpaz-Gargari E, Mesgari B, Rajaei Salmasi F (2008) Identification of a MIMO evaporator and its decentralized PID controller tuning using colonial competitive algorithm. IFAC World Congress, 11–12Google Scholar
  22. Richards A, Singh N (1996) Two level negotiations in bargaining over water. Proceedings of the International Game Theory Conference, Bangalore, India, 1–23Google Scholar
  23. Saaty TL (1994) Highlights and critical points in the theory and application of the analytical hierarchy process. Eur J Oper Res 74:426–447CrossRefGoogle Scholar
  24. Sepehri Rad H, Lucas C (2008) Application of imperialistic competition algorithm in recommender systems. In: 13th international CSI computer conference (CSICC’08), Kish Island, IranGoogle Scholar
  25. Talataharia S, Farahmand Azarb B, Sheikholeslamib R, Gandomic AH (2012) Imperialist competitive algorithm combined with chaos for global optimization. Commun Nonlinear Sci Numer Simul 17(3):1312–1319CrossRefGoogle Scholar
  26. Tang Y, Reed PM (2005) Multiobjective Tools and Strategies for Calibrating Integrated Models. Proc. ASCE/EWRI World Water and Envmtl. Resour. Cong., ASCE, Reston, VAGoogle Scholar
  27. Ziaei M, Teang Shui L, Goodarzi E (2012) Optimization and simulation modeling for operation of the Zayandehrud reservoir. Water Int J, Taylor & Francis 37(3):305–318Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Mohammad Karamouz
    • 1
    • 2
  • Sara Nazif
    • 2
  • Mohammad Ali Sherafat
    • 2
  • Zahra Zahmatkesh
    • 2
  1. 1.Polytechnic School of Engineering, New York UniversityBrooklynUSA
  2. 2.School of Civil Engineering, College of EngineeringUniversity of TehranTehranIran

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