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KSCE Journal of Civil Engineering

, Volume 22, Issue 11, pp 4668–4680 | Cite as

Optimization of Reservoir Operation using New Hybrid Algorithm

  • Zaher Mundher Yaseen
  • Hojat KaramiEmail author
  • Mohammad Ehteram
  • Nuruol Syuhadaa Mohd
  • Sayed Farhad Mousavi
  • Lai Sai Hin
  • Ozgur Kisi
  • Saeed Farzin
  • Sungwon Kim
  • Ahmed El-Shafie
Water Resources and Hydrologic Engineering

Abstract

Due to the scarcity of fresh water resources, exploiting dams’ reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers’ water needs with high reliability. In this research, a new hybrid approach of Artificial Fish Swarm Algorithm (AFSA) and Particle Swarm Optimization Algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid Algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant’s objective function.

Keywords

optimization of reservoir operation artificial intelligence artificial fish algorithm particle swarm optimization algorithm 

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References

  1. Adeyemo, J. A. (2011). “Reservoir operation using multi-objective evolutionary algorithms-a review.” Asian Journal of Scientific Research, Vol. 4, No. 1, pp. 16–27, DOI: 10.3923/ajsr.2011.16.27.CrossRefGoogle Scholar
  2. Afshar, A., Massoumi, F., Afshar, A., and Mariño, M. A. (2015). “State of the art review of ant colony optimization applications in water resource management.” Water Resources Management, Vol. 29, No. 11, pp. 3891–3904, DOI: 10.1007/s11269-015-1016-9.CrossRefGoogle Scholar
  3. Ahadi, A., Ghadimi, N., and Mirabbasi, D. (2015). “An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability.” Complexity, Vol. 21, No. 1, pp. 99–113, DOI: 10.1002/cplx.21546.CrossRefGoogle Scholar
  4. Ahmed, J. A. and Sarma, A. K. (2005). “Genetic algorithm for optimal operating policy of a multipurpose reservoir.” Water Resources Management, Vol. 19, No. 2, pp. 145–161, DOI: 10.1007/s11269-005-2704-7.CrossRefGoogle Scholar
  5. Akay, B. and Karaboga, D. (2012). “A modified Artificial Bee Colony algorithm for real-parameter optimization.” Information Sciences, Vol. 192, pp. 120–142, DOI: 10.1016/j.ins.2010.07.015.CrossRefGoogle Scholar
  6. Arunkumar, R. and Jothiprakash, V. (2012). “Optimal reservoir operation for hydropower generation using non-linear programming model.” Journal of The Institution of Engineers (India): Series A, Vol. 93, No. 2, pp. 111–120, DOI: 10.1007/s40030-012-0013-8.CrossRefGoogle Scholar
  7. Asgari, H.-R., Haddad, O. B., Pazoki, M., and Loáiciga, H. A. (2016). “Weed optimization algorithm for optimal reservoir operation.” Journal of Irrigation and Drainage Engineering, Vol. 142, No. 2, DOI: 10.1061/(ASCE)IR.1943-4774.0000963.Google Scholar
  8. Atashpaz-Gargari, E. and Lucas, C. (2007). “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition.” 2007 IEEE Congress on Evolutionary Computation, CEC 2007, pp. 4661–4667.CrossRefGoogle Scholar
  9. Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., and Loáiciga, H. (2014). “Development and application of the bat algorithm for optimizing the operation of reservoir systems.” Journal of Water Resources Planning and Management, pp. 4014097, DOI: 10.1061/(ASCE)WR.1943-5452.0000498.Google Scholar
  10. Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., and Loáiciga, H. A. (2015). “Development and application of the bat algorithm for optimizing the operation of reservoir systems.” Journal of Water Resources Planning and Management, Vol. 141, No. 8, pp. 4014097, DOI: 10.1061/(ASCE)WR.1943-5452.0000498.CrossRefGoogle Scholar
  11. Chaohua, D., Weirong, C., and Yunfang, Z. (2007). “Seeker optimization algorithm.” 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006, pp. 225–229.Google Scholar
  12. Eberhart, R. C. (2001). “Fuzzy adaptive particle swarm optimization.” Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Vol. 1, pp. 101–106, DOI: 10.1109/CEC.2001.934377.CrossRefGoogle Scholar
  13. Fang, H., Hu, T., Zeng, X., and Wu, F. (2014). “Simulation-optimization model of reservoir operation based on target storage curves.” Water Science and Engineering, Vol. 7, No. 4, pp. 433–445, DOI: 10.3882/j.issn.1674-2370.2014.04.008.Google Scholar
  14. Haddad, O. B., Afshar, A., and Mariño, M. A. (2011). “Multireservoir optimisation in discrete and continuous domains.” Proceedings of the Institution of Civil Engineers -Water Management, Vol. 164, No. 2, pp. 57–72, DOI: 10.1680/wama.900077.CrossRefGoogle Scholar
  15. Hong, W. C. (2011). “Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm.” Energy, Vol. 36, No. 9, pp. 5568–5578, DOI: 10.1016/j.energy.2011.07.015.CrossRefGoogle Scholar
  16. Hosseini-Moghari, S. M., Morovati, R., Moghadas, M., and Araghinejad, S. (2015). “Optimum operation of reservoir using two evolutionary algorithms: Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA).” Water Resources Management, Vol. 29, No. 10, pp. 3749–3769, DOI: 10.1007/s11269-015-1027-6.CrossRefGoogle Scholar
  17. Javidan, J. and Ghasemi, A. (2013). “A novel fuzzy RPID controller for multiarea AGC with IABC optimization.” Journal of Engineering (United States), Vol. 2013, DOI: 10.1155/2013/510572.Google Scholar
  18. Jiang, J., Bo, Y., Song, C., and Bao, L. (2012). “Hybrid algorithm based on particle swarm optimization and artificial fish swarm algorithm.” International Symposium on Neural Networks, pp. 607–614.Google Scholar
  19. Jothiprakash, V., Shanthi, G., and Arunkumar, R. (2011). “Development of operational policy for a multi-reservoir system in india using genetic algorithm.” Water Resources Management, Vol. 25, No. 10, pp. 2405–2423, DOI: 10.1007/s11269-011-9815-0.CrossRefGoogle Scholar
  20. Khanmirzaei, Z., Teshnehlab, M., and Sharifi, A. (2010). “Modified honey bee optimization for recurrent neuro-fuzzy system model.” 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010, pp. 780–785.Google Scholar
  21. Kulkarni, R. V. and Venayagamoorthy, G. K. (2010). “Adaptive critics for dynamic optimization.” Neural Networks, Vol. 23, No. 5, pp. 587–591, DOI: 10.1016/j.neunet.2010.02.002.CrossRefGoogle Scholar
  22. Liang, J. J., Qu, B. Y., Suganthan, P. N., and Hernández-Díaz, A. G. (2013). “Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization.”Google Scholar
  23. Maleksaeedi, I., Khiav, B. E., Germi, M. B., and Ghadimi, N. (2015). “A new two-stage algorithm for solving power flow tracing.” Complexity, Vol. 21, No. 1, pp. 187–194.CrossRefGoogle Scholar
  24. Narasimhan, H. (2009). “Parallel Artificial Bee Colony (PABC) algorithm.” 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 -Proceedings, pp. 306–311.CrossRefGoogle Scholar
  25. Passino, K. M. (2002). “Biomimicry of bacterial foraging for distributed optimization and control.” Control Systems, IEEE, Vol. 22, No. 3, pp. 52–67, DOI: 10.1109/MCS.2002.1004010.MathSciNetCrossRefGoogle Scholar
  26. Peng, Y. (2011). “An improved artificial fish swarm algorithm for optimal operation of cascade reservoirs.” Journal of Computers, Vol. 6, No. 4, pp. 740–746, DOI: 10.4304/jcp.6.4.740-746.CrossRefGoogle Scholar
  27. Shayanfar, H. A., Barazandeh, E. S., Seyed Shenava, S. J., Ghasemi, A., and Abedinia, O. (2012). “Solving optimal unit commitment by improved honey bee mating optimization.” International Journal on “Technical and Physical Problem of Engineering” (IJTPE), Vol. 4, No. 13, pp. 38–45.Google Scholar
  28. Shen, W., Guo, X., Wu, C., and Wu, D. (2011). “Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm.” Knowledge-Based Systems, Vol. 24, No. 3, pp. 378–385, DOI: 10.1016/j.knosys.2010.11.001.CrossRefGoogle Scholar
  29. Tasgetiren, M. F., Pan, Q. K., Suganthan, P. N., and Chen, A. H. L. (2011). “A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops.” Information Sciences, Vol. 181, No. 16, pp. 3459–3475, DOI: 10.1016/j.ins.2011.04.018.MathSciNetCrossRefGoogle Scholar
  30. Wang, L. and Li, L. P. (2013). “An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems.” International Journal of Electrical Power and Energy Systems, Vol. 44, No. 1, pp. 832–843, DOI: 10.1016/j.ijepes.2012.08.021.CrossRefGoogle Scholar
  31. Zeng, X., Tao, J., Zhang, P., Pan, H., and Wang, Y.-Y. (2012). “Reactive power optimization of wind farm based on improved genetic algorithm.” Energy Procedia, Vol. 14, pp. 1362–1367.CrossRefGoogle Scholar
  32. Zhang, Z., Long, K., Wang, J., and Dressler, F. (2014). “On swarm intelligence inspired self-organized networking: Its bionic mechanisms, designing principles and optimization approaches.” IEEE Communications Surveys and Tutorials, Vol. 16, No. 1, pp. 513–537, DOI: 10.1109/SURV.2013.062613.00014.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers 2018

Authors and Affiliations

  • Zaher Mundher Yaseen
    • 1
  • Hojat Karami
    • 2
    Email author
  • Mohammad Ehteram
    • 2
  • Nuruol Syuhadaa Mohd
    • 3
  • Sayed Farhad Mousavi
    • 2
  • Lai Sai Hin
    • 3
  • Ozgur Kisi
    • 4
  • Saeed Farzin
    • 2
  • Sungwon Kim
    • 5
  • Ahmed El-Shafie
    • 3
  1. 1.Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Dept. of Water Engineering and Hydraulic Structures, Faculty of Civil EngineeringSemnan UniversitySemnanIran
  3. 3.Civil Engineering Dept., Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  4. 4.School of Natural Sciences and EngineeringIlia State UniversityTbilisiGeorgia
  5. 5.Dept. of Railroad Construction and Safety EngineeringDongyang UniversityYeongjuKorea

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