Evolving Systems

, Volume 8, Issue 4, pp 287–301 | Cite as

Constrained improved particle swarm optimization algorithm for optimal operation of large scale reservoir: proposing three approaches

  • R. Moeini
  • M. Babaei
Original Paper


In this paper, the improved particle swarm optimization (IPSO) algorithm is used to solve large scale reservoir operation optimization problem proposing unconstrained and two constrained versions of this algorithm. In the two constrained versions proposed for the IPSO algorithm, named PCIPSO and FCIPSO, each particle may be forced to satisfy problem constraints during solution building. By considering water releases or storage volumes at each operation time period as decision variable of the problem, here, two formulations are proposed for each version. In the second proposed constrained version algorithm (FCIPSO), at first, the water storage volume bounds are modified in order to recognize the infeasible components of the search space and exclude from the search process before the main search starts. This mechanism leads to smaller search space size for the problem and finally better results. The simple and hydropower operation problems of “Dez” reservoir in the southern Iran over 60, 240 and 480 monthly operations time periods are solved here using both proposed formulations of theses algorithms and the results are presented and compared with other available results. The results show the capability of the proposed algorithms and especially the second constrained version of the IPSO algorithm, FCIPSO, to optimally solve the reservoir operation optimization problem. In other words, the results of both formulations of constrained IPSO and especially FCIPSO algorithm are improved significantly in comparison with unconstrained IPSO algorithm over all operations time periods of simple and hydropower operation of the reservoir.


Optimal operation of reservoir Large scale problem Explicitly constraints handling Improved particle swarm optimization algorithm Dez reservoir 


  1. Afshar MH (2012) Large scale reservoir operation by Constrained Particle Swarm Optimization algorithms. Hydro Environ Res 6:75–87. doi: 10.1016/j.jher.2011.04.003 CrossRefMathSciNetGoogle Scholar
  2. Afshar MH (2013) Extension of the constrained particle swarm optimization algorithm to optimal operation of multi-reservoirs system. Electr Power Energ Syst 51:71–81. doi: 10.1016/j.ijepes.2013.02.035 CrossRefGoogle Scholar
  3. Afshar MH, Moeini R (2008) Partially and fully constrained ant algorithms for the optimal solution of large scale reservoir operation problems. Water Resour Manag 22(1):1835–1857. doi: 10.1007/s11269-008-9256-6 CrossRefGoogle Scholar
  4. Ahmad A, El-Shafie A, Razali, S.F.M., Mohamad ZS (2014) Reservoir optimization in water resources: a review. Water Resour Manag 28:3391–3405. doi: 10.1007/s11269-014-0700-5 CrossRefGoogle Scholar
  5. Barros M, Tsai F, Yang S, Lopes JEG, Yeh W (2003) Optimization of large-scale hydropower systems operations. Water Resour Plan Manag 129(3):178–188. doi: 10.1061/(ASCE)0733-9496 CrossRefGoogle Scholar
  6. Basu M (2005) A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems. Int J Electr Power Energ Syst 27(2):147–153. doi: 10.1016/j.ijepes.2004.09.004 CrossRefGoogle Scholar
  7. Becker L, Yeh W (1974) Optimization of real-time operation of a multiple reservoir system. Water Resour Res 10(6):1107–1112. doi: 10.1029/WR010i006p01107 CrossRefGoogle Scholar
  8. Bozorg Haddad O, Afshar A, Marino MA (2006) Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour Manag 20(5):661–680. doi: 10.1007/s11269-005-9001-3 CrossRefGoogle Scholar
  9. Bozorg Haddad O, Afshar A, Marino MA (2008) Design operation of multi-hydropower reservoirs: HBMO approach. Water Resour Manag 22(12):1709–1722. doi: 10.1007/s11269-008-9249-5 CrossRefGoogle Scholar
  10. Braga BPF, Yeh WW-G, Becker L, Burrows MTL (1991) Stochastic optimization of multiple-reservoir-system operation. Water Resour Plan Manag 117(4):471–481. doi: 10.1061/(ASCE)0733-9496 CrossRefGoogle Scholar
  11. Cai X, Mckinney DC, Larson LS (2002) Piece-by-piece approach to solving large nonlinear water resources management models. Water Resour Plan Manag 127, 6, 363–368. doi: 10.1061/(ASCE)0733-9496 CrossRefGoogle Scholar
  12. Chang FJ, Chen L (1998) Real-coded genetic algorithm for rule based flood control reservoir management. Water Resour Manag 12(3):185–198. doi: 10.1023/A:1007900110595 CrossRefMathSciNetGoogle Scholar
  13. Chang FJ, Chen L, Chang LC (2005) Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol Process 19(11):2277–2289. doi: 10.1002/hyp.5674 CrossRefGoogle Scholar
  14. Chen L (2003) Real time genetic algorithm optimization of long term reservoir operation. Am Water Resour Assoc 39(5):1157–1165CrossRefGoogle Scholar
  15. Chen L, Chang FJ (2007) Applying a real-coded multi population genetic algorithm to multi-reservoir operation. Hydrol Process 21(5):688–698. doi: 10.1002/hyp.6259 CrossRefMathSciNetGoogle Scholar
  16. Clerc M (2006) Confinements and biases in particle swarm optimization [Online].
  17. Eberhart RC, Simpson P, Dobbins R (1996) Computational Intelligence PC Tools. Academic Press Professional Inc, San Diego, CAGoogle Scholar
  18. Esat V, Hall MJ, 1994. Water resource system optimization using genetic algorithms. Hydro informatics’94, Proceeding 1st International Conference on Hydro informatics, Balkerma, Rotterdam, The Netherlands, pp. 225–231Google Scholar
  19. Fahmy HS, King JP, Wentzle MW, Seton JA, 1994. Economic optimization of river management using genetic algorithms. International summer Meeting, AM. Soc. Agric. Engrs, paper no.943034, St. Joseph, MichiganGoogle Scholar
  20. Helwig S, Branke J, Mostaghim S (2013) Experimental analysis of bound handling techniques in particle swarm optimization. IEEE Trans Evol Comput 17(2):259–271. doi: 10.1109/TEVC.2012.2189404 CrossRefGoogle Scholar
  21. Hınçal O, Altan-Sakarya AB, Ger AM (2011) Optimization of multireservoir systems by genetic algorithm. Water Resour Manag 25:1465–1487. doi: 10.1007/s11269-010-9755-0 CrossRefGoogle Scholar
  22. Hossain MS, El-shafie A (2013a) Intelligent systems in optimizing reservoir operation policy: a review. Water Resour Manag 27:3387–3407. doi: 10.1007/s11269-013-0353-9 CrossRefGoogle Scholar
  23. Hossain MS, El-shafie A (2013b) Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam. Neural Comput Applic 24(5):1199–1206. doi: 10.1007/s00521-012-1309-3 CrossRefGoogle Scholar
  24. Hossain MS, El-shafie A (2014) Evolutionary techniques versus swarm intelligences: application in reservoir release optimization. Neural Comput Applic 24:1583–1594. doi: 10.1007/s00521-013-1389-8 CrossRefGoogle Scholar
  25. Jabr R, Coonick A, Cory B (2000) A homogeneous linear programming algorithm for the security constrained economic dispatch problem. IEEE Trans Power Syst 15:930–937. doi: 10.1109/59.871715 CrossRefGoogle Scholar
  26. Jalali MR (2005) Optimal design and operation of hydro systems by ant colony algorithms: new heuristic approach. PhD Thesis, Department of Civil Engineering, Iran University of Science and Technology.Google Scholar
  27. Jalali MR, Afshar A, Marino MA (2007) Multi-colony ant algorithm for continuous multi-reservoir operation optimization problems. Water Resour Res 21(9):1429–1447. doi: 10.1007/s11269-006-9092-5 Google Scholar
  28. Kelman J, Stedinger JR, Cooper LA, Hsu E, Yuan S-Q (1990) Sampling stochastic dynamic programming applied to reservoir operation. Water Resour Res 26(3):447–454. doi: 10.1029/WR026i003p00447 CrossRefGoogle Scholar
  29. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks IV. Piscataway, NJ, pp 1942–1948Google Scholar
  30. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Academic Press, LondonGoogle Scholar
  31. Kuczera G (1989) Fast multi reservoir multi-period linear programming models. Water Resour Res 25(2):169–176. doi: 10.1029/WR025i002p00169 CrossRefGoogle Scholar
  32. Kumar DN, Baliarsingh F (2003) Folded dynamic programming for optimal operation of multi-reservoir system. Water Resour Manag 17(5):337–353. doi: 10.1023/A:1025894500491 CrossRefGoogle Scholar
  33. Kumar DN, Reddy MJ (2006) Ant colony optimization for multi-purpose reservoir operation. Water Resour Plan Manag 20:879–898. doi: 10.1007/s11269-005-9012-0 CrossRefGoogle Scholar
  34. Kumar DN, Reddy MJ, 2007. Multipurpose reservoir operation using particle swarm optimization. Water Resour Plan Manag 133(3):192–201. doi: 10.1061/(ASCE)0733-9496 CrossRefGoogle Scholar
  35. Labadie J, 2004. Optimal operation of multireservoir systems: state-of-the-art review. Water Resour Plan Manag 2(93):93–111 doi: 10.1061/(ASCE)0733-9496(2004)130:2(93)CrossRefGoogle Scholar
  36. Li Y, Zhou J, Zhang Y, Qin H, Liu L, 2010. Novel multiobjective shuffled frog leaping algorithm with application to reservoir flood control operation. Water Resour Plan Manag 136:217–226. doi: 10.1061/(ASCE)WR.1943-5452.0000027 CrossRefGoogle Scholar
  37. Liu X, Guo S, Liu P, Chen L, Li X (2011) Deriving optimal refill rules for multi-purpose reservoir operation. Water Resour Manag 25:431–448. doi: 10.1007/s11269-010-9707-8 CrossRefGoogle Scholar
  38. Loucks DP, Stedinger JR, Haith DA (1981) Water resource systems planning and analysis. Prentice-Hall, Englewood CliffsGoogle Scholar
  39. Madadgar S, Afshar A (2009) An improved continuous ant algorithm for optimization of water resources problems. Water Resour Manag 23(10):2119–2139. doi: 10.1007/s11269-008-9373-2 CrossRefGoogle Scholar
  40. Marino MA, Loaiciga HA (1985) Dynamic model for multi reservoir operation. Water Resour Res 21(5):619–630. doi: 10.1029/WR021i005p00619 CrossRefGoogle Scholar
  41. Moeini R, Afshar MH (2011) Arc based constrained ant colony optimisation algorithms for the optimal solution of hydropower reservoir operation problems. Can J Civil Eng 38(7):811–824. doi: 10.1139/l11-051 Google Scholar
  42. Moeini R, Afshar MH (2013) Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems. Hydroinformatics 15(1):155–173. doi: 10.2166/hydro.2012.081 CrossRefGoogle Scholar
  43. Mousavi J, Karamouz M (2003) Computational improvement for dynamic programming models by diagnosing infeasible storage combinations. Adv Water Resour 26(8):851–859. doi: 10.1016/S0309-1708(03)00061-7 CrossRefGoogle Scholar
  44. Oliveira R, Loucks D (1997) Operation rules for multi reservoir systems. Water Resour Res 33(4):839–852. doi: 10.1029/96WR03745 CrossRefGoogle Scholar
  45. Peng CH, Buras N (2000) Dynamic operation of a surface water resources system. Water Resour Res 36(9):2701–2709. doi: 10.1029/2000WR900169 CrossRefGoogle Scholar
  46. Perera BJC, Conder GP (1998) Computational improvement for stochastic dynamic programming models for urban water supply reservoirs. Am Water Resour Assoc 34(2):267–278. doi: 10.1111/j.1752-1688.1998.tb04133 CrossRefGoogle Scholar
  47. Shi Y, Eberhart RC (1998a) Parameter selection in particle swarm optimization. In: Porto VW, Saravanan N, Waagen D, Eiben AE (eds) Evolutionary programming, vol. VII. Springer, New York, pp. 611–616Google Scholar
  48. Shi Y, Eberhart RC (1998b) A Modified Particle Swarm Optimizer. In: Proceedings of the IEEE World Congress on Computational Intelligence, pp. 69–73Google Scholar
  49. Srinivasan K, Neelakantan TR, Narayan PS, Kumar CN, 1999 Mixed-integer model for reservoir performance optimization. Water Resour Plan Manag 125(5):298–301. doi: 10.1061/(ASCE)0733-9496 CrossRefGoogle Scholar
  50. Tu MY, Hsu NS, Yeh WWG (2003) Optimization of reservoir management and operation with hedging rules. Water Resour Plan Manag 129(2):86–97. doi: 10.1061/(ASCE)0733-9496 CrossRefGoogle Scholar
  51. Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. Water Resour Plan Manag 125(1):25–33. doi: 10.1061/(ASCE)0733-9496 CrossRefGoogle Scholar
  52. Yeh WG (1985) Reservoir management and operations models: a state-of-the-art review. Water Resour Res 21(12):1797–1818. doi: 10.1029/WR021i012p01797 CrossRefGoogle Scholar
  53. Yoo JH (2009) Maximization of hydropower generation through the application of a linear programming model. J Hydrol 376(1):182–187. doi: 10.1016/j.jhydrol.2009.07.026 CrossRefGoogle Scholar
  54. Zhang Z, Zhang S, Wang Y, Jiang Y, Wang H (2013) Use of parallel deterministic dynamic programming and hierarchical adaptive genetic algorithm for reservoir operation optimization. Comput Ind Eng 65:310–321. doi: 10.1016/j.cie.2013.02.003 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of Civil Engineering and TransportationUniversity of IsfahanIsfahanIran

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