A hybrid bat–swarm algorithm for optimizing dam and reservoir operation

Abstract

One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consume for convergence. Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. However, within the EA, there is a need to assume internal parameters at the initial stage of the model development, such assumption might increase the ambiguity of the model outputs. This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat–swarm algorithm (HB-SA). The main idea behind this hybridization is to improve the BA by using the PSOA in parallel to replace the suboptimal solution generated by the BA. The solutions effectively speed up the convergence procedure and avoid the trapping in local optima caused by using the BA. The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. The results showed that the proposed HB-SA algorithm can achieve minimum irrigation deficits during the examined period and outperforms the other optimization algorithms. In addition, the computational time for the convergence procedure is reduced using the HB-SA. The proposed HB-SA is successfully examined and can be generalized for several dams and reservoir systems around the world.

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References

  1. 1.

    Chau K (2004) River stage forecasting with particle swarm optimization. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, Berlin, pp 1166–1173

    Google Scholar 

  2. 2.

    Wu CL, Chau KW (2011) Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399(3–4):394–409

    Article  Google Scholar 

  3. 3.

    Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597

    Google Scholar 

  4. 4.

    Xie A, Liu P, Guo S, Zhang X, Jiang H, Yang G (2018) Optimal design of seasonal flood limited water levels by jointing operation of the reservoir and floodplains. Water Resour Manage 32(1):179–193

    Article  Google Scholar 

  5. 5.

    Afshar MH, Hajiabadi R (2018) A novel parallel cellular automata algorithm for multi-objective reservoir operation optimization. Water Resources Manag 32(2):785–803

    Article  Google Scholar 

  6. 6.

    Cheng CT, Chau KW (2004) Flood control management system for reservoirs. Environ Model Softw 19(12):1141–1150

    Article  Google Scholar 

  7. 7.

    Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Faizollahzadeh Ardabili S, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: challenges, research directions and future work. Eng Appl Comput Fluid Mech 12(1):411–437

    Google Scholar 

  8. 8.

    Han C, Zheng B, Qin Y, Ma Y, Yang C, Liu Z, Chi M (2018) Impact of upstream river inputs and reservoir operation on phosphorus fractions in water-particulate phases in the Three Gorges Reservoir. Sci Total Environ 610:1546–1556

    Article  Google Scholar 

  9. 9.

    Gauvin C, Delage E, Gendreau M (2018) A stochastic program with time series and affine decision rules for the reservoir management problem. Eur J Oper Res 267(2):716–732

    MathSciNet  MATH  Article  Google Scholar 

  10. 10.

    Zouache D, Moussaoui A, Abdelaziz FB (2018) A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem. Eur J Oper Res 264(1):74–88

    MathSciNet  MATH  Article  Google Scholar 

  11. 11.

    Zéphyr L, Lang P, Lamond BF, Côté P (2017) Approximate stochastic dynamic programming for hydroelectric production planning. Eur J Oper Res 262(2):586–601

    MathSciNet  MATH  Article  Google Scholar 

  12. 12.

    Yin P-Y, Glover F, Laguna M, Zhu J-X (2010) Cyber Swarm Algorithms – Improving particle swarm optimization using adaptive memory strategies. Eur J Oper Res 201:377–389

    MathSciNet  MATH  Article  Google Scholar 

  13. 13.

    Liu R, Li J, Fan J, Mu C, Jiao L (2017) A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization. Eur J Oper Res 261(3):1028–1051

    MathSciNet  MATH  Article  Google Scholar 

  14. 14.

    Séguin S, Fleten S-E, Côté P, Pichler A, Audet C (2017) Stochastic short-term hydropower planning with inflow scenario trees. Eur J Oper Res 259(3):1156–1168

    MathSciNet  MATH  Article  Google Scholar 

  15. 15.

    Marinakis Y, Migdalas A, Sifaleras A (2017) A hybrid particle swarm optimization—variable neighborhood search algorithm for constrained shortest path problems. Eur J Oper Res 261(3):819–834

    MathSciNet  MATH  Article  Google Scholar 

  16. 16.

    Uysal G, Schwanenberg D, Alvarado-Montero R, Şensoy A (2018) Short term optimal operation of water supply reservoir under flood control stress using model predictive control. Water Resources Manag 32:1–15

    Article  Google Scholar 

  17. 17.

    Bozorg-Haddad O, Azarnivand A, Loáiciga HA (2018) Closure to “development of a comparative multiple criteria framework for ranking pareto optimal solutions of a multiobjective reservoir operation problem” by Omid Bozorg-Haddad, Ali Azarnivand, Seyed-Mohammad Hosseini-Moghari, and Hugo A. Loáiciga. J Irrig Drain Eng 144(4):07018006

    Article  Google Scholar 

  18. 18.

    Taormina R, Chau KW, Sivakumar B (2015) Neural network river forecasting through baseflow separation and binary-coded swarm optimization. J Hydrol 529:1788–1797

    Article  Google Scholar 

  19. 19.

    Afshar A, Shafii M, Haddad OB (2011) Optimizing multi-reservoir operation rules: an improved HBMO approach. J Hydroinform 13(1):121–139

    Article  Google Scholar 

  20. 20.

    Fallah-Mehdipour E, Haddad OB, Mariño MA (2012) Real-time operation of reservoir system by genetic programming. Water Resour Manag 26(14):4091–4103

    Article  Google Scholar 

  21. 21.

    Hossain MS, El-Shafie A (2014) Evolutionary techniques versus swarm intelligences: application in reservoir release optimization. Neural Comput Appl 24(7–8):1583–1594

    Article  Google Scholar 

  22. 22.

    Bolouri-Yazdeli Y, Haddad OB, Fallah-Mehdipour E, Mariño MA (2014) Evaluation of real-time operation rules in reservoir systems operation. Water Resour Manag 28(3):715–729

    Article  Google Scholar 

  23. 23.

    Haddad OB, Hosseini-Moghari SM, Loáiciga HA (2015) Biogeography-based optimization algorithm for optimal operation of reservoir systems. J Water Resources Plan Manag 142(1):04015034

    Article  Google Scholar 

  24. 24.

    Asgari HR, Bozorg Haddad O, Pazoki M, Loáiciga HA (2015) Weed optimization algorithm for optimal reservoir operation. J Irrig Drain Eng 142(2):04015055

    Article  Google Scholar 

  25. 25.

    Hosseini-Moghari SM, Morovati R, Moghadas M, Araghinejad S (2015) Optimum operation of reservoir using two evolutionary algorithms: imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water Resour Manag 29(10):3749–3769

    Article  Google Scholar 

  26. 26.

    Hossain MS, El-Shafie A, Mahzabin MS, Zawawi MH (2016) System performances analysis of reservoir optimization–simulation model in application of artificial bee colony algorithm. Neural Comput Appl 30:1–12

    Google Scholar 

  27. 27.

    Mohammadrezapour O, Yoosefdoost I, Ebrahimi M (2017) Cuckoo optimization algorithm in optimal water allocation and crop planning under various weather conditions (case study: Qazvin plain, Iran). Neural Comput Appl. https://doi.org/10.1007/s00521-017-3160-z

    Article  Google Scholar 

  28. 28.

    Ehteram M, Karami H, Mousavi SF, Farzin S, Kisi O (2017) Optimization of energy management and conversion in the multi-reservoir systems based on evolutionary algorithms. J Clean Prod 168:1132–1142

    Article  Google Scholar 

  29. 29.

    Ehteram M, Mousavi SF, Karami H, Farzin S, Emami M, Othman FB, El-Shafie A (2017) Fast convergence optimization model for single and multi-purposes reservoirs using hybrid algorithm. Adv Eng Inform 32:287–298

    Article  Google Scholar 

  30. 30.

    Ming B, Liu P, Bai T, Tang R, Feng M (2017) Improving optimization efficiency for reservoir operation using a search space reduction method. Water Resour Manag 31(4):1173–1190

    Article  Google Scholar 

  31. 31.

    Karami H, Ehteram M, Mousavi SF, Farzin S, Kisi O, El-Shafie A (2018) Optimization of energy management and conversion in the water systems based on evolutionary algorithms. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3412-6

    Article  Google Scholar 

  32. 32.

    Ahmadianfar I, Adib A, Salarijazi M (2015) Optimizing multireservoir operation: hybrid of bat algorithm and differential evolution. J Water Resources Plan Manag 142(2):05015010

    Article  Google Scholar 

  33. 33.

    Bozorg-Haddad O, Karimirad I, Seifollahi-Aghmiuni S, Loáiciga HA (2014) Development and application of the bat algorithm for optimizing the operation of reservoir systems. J Water Resources Plan Manag 141(8):04014097

    Article  Google Scholar 

  34. 34.

    Ghanem WA, Jantan A (2017) An enhanced bat algorithm with mutation operator for numerical optimization problems. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3021-9

    Article  Google Scholar 

  35. 35.

    Chakri A, Yang XS, Khelif R, Benouaret M (2017) Reliability-based design optimization using the directional bat algorithm. Neural Comput Appl 30:1–22

    Google Scholar 

  36. 36.

    Jalal M, Mukhopadhyay AK, Goharzay M (2018) Bat algorithm as a metaheuristic optimization approach in materials and design: optimal design of a new float for different materials. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3430-4

    Article  Google Scholar 

  37. 37.

    Qaderi K, Akbarifard S, Madadi MR, Bakhtiari B (2017) Optimal operation of multi-Thomas Telford Ltd. reservoirs by water cycle algorithm. In: Proceedings of the Institution of Civil Engineers—Water Management, pp 1–12

Download references

Acknowledgements

This research was funded by a University of Malaya Research Grant “UMRG” (RP025A-18SUS). The authors are grateful to the University of Malaya, Malaysia, for supporting this study. The authors also would like to acknowledge the Universiti Tenaga Nasional for the financial support under Bold Grant 10289176/B/9/2017/14.

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Correspondence to Ahmed El-Shafie.

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Yaseen, Z.M., Allawi, M.F., Karami, H. et al. A hybrid bat–swarm algorithm for optimizing dam and reservoir operation. Neural Comput & Applic 31, 8807–8821 (2019). https://doi.org/10.1007/s00521-018-3952-9

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Keywords

  • Particle swarm optimization
  • Multireservoir system
  • Bat algorithm
  • Optimization model