Developing Self-adaptive Melody Search Algorithm for Optimal Operation of Multi-reservoir Systems

Document Type : Research Paper

Authors

1 Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Department of Irrigation and Drainage, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Environmental Sciences Research Center, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Tehran, Iran.

Abstract

Operation of multi-reservoir systems is known as complicated and often large-scale optimization problems. The problems, because of broad search space, nonlinear relationships, correlation of several variables, as well as problem uncertainty, are difficult requiring powerful algorithms with specific capabilities to be solved. In the present study a Self-adaptive version of Melody Search algorithm is presented and applied to obtain Operating Rule Curves for multi-reservoir systems. The self-adaptive mechanism is implemented to satisfy problems constraints and perform algorithm parameters evolution going through different iterations. The research initially evaluates capability of extended algorithm using eight benchmark problems comparing other well-known metaheuristic algorithms, and verifies its effectiveness. Then, the algorithm is adopted for optimal operation of a four-reservoir system located in Karkheh river basin to properly meet agricultural requirements and to decrease the probability of major failures; and finally, the results are provided.

Keywords


  1. Oliveira R., and Loucks D.P. (1997). Operating Rules for Multi-reservoir Systems. Water Resources Res., 33, 839-852.
  2. Wang KW, Chang LC, Chang FJ, (2011). Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Adv Water Resour; 34(10):1343-1351.
  3. Mahootchi M, Ponnambalam K, Tizhoosh H. (2010). Operations optimization of multireservoir systems using storage moments equation. Adv Water Resour; 33(9):1150-1163.
  4. Chaves P, Chang FJ, (2008). Intelligent reservoir operation system based on evolving artificial neural networks. Adv Water Resour; 31(6):926-936.
  5. Moazami, S., Abdollahipour, A., Zakeri Niri, M., & Ashrafi, S. M. (2016). Hydrological Assessment of Daily Satellite Precipitation Products over a Basin in Iran. Journal of Hydraulic Structures, 2(2), 35-45.
  6. Wardlaw R, Sharif M, (1999). Evaluation of Genetic Algorithms for Optimal Reservoir System Operation. Journal of Water Resources Planning and Management 125(1): 25-33.
  7. Sharif M, Wardlaw R, (2000). Multireservoir systems optimization using genetic algorithms: Case study. J. Comput Civ Eng, 14(4): 255–263.
  8. Suiadee W, Tingsanchali T, (2007). A combined simulation–genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project, Thailand. Hydrol. Process 2007; 21(23): 3211–3225.
  9. Dariane AB, Momtahen Sh, (2009), “Optimization of multireservoir systems operation using modified direct search genetic algorithm”, J. Water Resour Plann Manage; 135(3): 141–148.
  10. Jalali MR, Afshar A, Marino MA, (2003), “Reservoir Operation by Ant Colony Optimization Algorithms”, online paper; http://www.optimizationonline.org/DB FILE/2003/07/696.pdf, (accessed on 10/12/2004).
  11. Kumar D. N., Reddy M. J. (2006). Ant colony optimization for multi-purpose reservoir operation. Water Resour Manage; 20(6): 879-898.
  12. Kumar D. N., Reddy M. J. (2007). Multipurpose reservoir operation using particle swarm optimization. J. Water Resour Plann Manage. 133(3): 192-201.
  13. Fu X, Li A, Wang L, Ji C. (2011). Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization. Computers and Mathematics with Applications; 62 (6): 2463–2471.
  14. Ostadrahimi L, Mariño MA, Afshar A, (2012). Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach. Water Resour Manage; 26:407–427.
  15. Fang, H. B., Hu, T. S., Zeng, X., & Wu, F. Y. (2014). Simulation-optimization model of reservoir operation based on target storage curves. Water Science and Engineering, 7(4), 433-445.
  16. Ashrafi S. M. (2015). Multi-reservoir Optimal Operation using Efficient Adaptive Melody Search Algorithm (EAMS). Proceeding of 10th International Congress on Civil Engineering, University of Tabriz, Tabriz, Iran.
  17. Zhou, Y., Guo, S., Liu, P., Xu, C. Y., & Zhao, X. (2016). Derivation of water and power operating rules for multi-reservoirs. Hydrological Sciences Journal, 61(2), 359-370.
  18. Ashrafi, S. M., & Dariane, A. B. (2017). Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems. Water Resources Management, 31(14), 4505-4520.
  19. Ak, M., Kentel, E., & Savasaneril, S. (2017). Operating policies for energy generation and revenue management in single-reservoir hydropower systems. Renewable and Sustainable Energy Reviews, 78, 1253-1261.
  20. Bozorg-Haddad, O., Athari, E., Fallah-Mehdipour, E., & Loáiciga, H. A. (2017). Real-time water allocation policies calculated with bankruptcy games and genetic programing. Water Science and Technology: Water Supply, ws2017102.
  21. Ashrafi, S. M., & Dariane, A. B. (2011). A novel and effective algorithm for numerical optimization: melody search (MS). In Hybrid Intelligent Systems (HIS), 2011 11th International Conference on (pp. 109-114). IEEE.
  22. Ashrafi, S. M., & Dariane, A. B. (2012). Application of Improved Harmony Search Algorithm in Optimal Operation of Multi-purpose Reservoirs. Proceeding of 9th International Congress on Civil Engineering, Isfahan, Iran.
  23. Ashrafi, S. M., & Dariane, A. B. (2013). Performance evaluation of an improved harmony search algorithm for numerical optimization: Melody Search (MS). Engineering applications of artificial intelligence, 26(4), 1301-1321.
  24. Kourabbaslou, N. E., Ashrafi S.M., Mohaghar A. (2016). Multi-objective Supply Chain Optimization Using Harmony Search Algorithm. Proceeding of the First International Conference on Industrial Engineering and Management, Tehran University, Tehran, Iran.
  25. Ashrafi, S. M., & Kourabbaslou, N. E. (2015). An Efficient Adaptive Strategy for Melody Search Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 6(3), 1-37.
  26. Xiang, W. L., An, M. Q., Li, Y. Z., He, R. C., & Zhang, J. F. (2014). An improved global-best harmony search algorithm for faster optimization. Expert Systems with Applications, 41(13), 5788-5803.
  27. Zhao, F., Liu, Y., Zhang, C., & Wang, J. (2015). A self-adaptive harmony PSO search algorithm and its performance analysis. Expert Systems with Applications, 42(21), 7436-7455.
  28. Koupaei, J. A., Hosseini, S. M. M., & Ghaini, F. M. (2016). A new optimization algorithm based on chaotic maps and golden section search method. Engineering Applications of Artificial Intelligence, 50, 201-214.
  29. Shivaie, M., & Ameli, M. T. (2016). Strategic multiyear transmission expansion planning under severe uncertainties by a combination of melody search algorithm and Powell heuristic method. Energy, 115, 338-352.
  30. Kiani-Moghaddam, M., & Shivaie, M. (2017). An Innovative Multi-Stage Multi-Dimensional Multiple-Inhomogeneous Melody Search Algorithm: Symphony Orchestra Search. In Bio-Inspired Computing for Information Retrieval Applications (pp. 1-40). IGI Global.
  31. Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer methods in applied mechanics and engineering, 194(36), 3902-3933.
  32. Qin AK, Huang VL and Suganthan PN, (2009), Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization. IEEE transaction on evolutionary computation, Vol. 13, No. 2, April 2009, pp. 398-417.
  33. Gao W., Liu S., Huang L., (2012), “A global best artificial bee colony algorithm for global optimization”, Journal of Computational and Applied Mathematics 236, 2741–2753.
  34. Kao YT, Zahara E, (2008), “A hybrid genetic algorithm and particle swarm optimization for multimodal functions”, Applied Soft Computing 8, 849–857.