IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p933-d1024985.html
   My bibliography  Save this article

Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups

Author

Listed:
  • Ji He

    (School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450011, China)

  • Xiaoqi Guo

    (School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450011, China)

  • Haitao Chen

    (School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450011, China)

  • Fuxin Chai

    (Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Shengming Liu

    (School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450011, China)

  • Hongping Zhang

    (Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Wenbin Zang

    (Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Songlin Wang

    (School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450011, China)

Abstract

The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

Suggested Citation

  • Ji He & Xiaoqi Guo & Haitao Chen & Fuxin Chai & Shengming Liu & Hongping Zhang & Wenbin Zang & Songlin Wang, 2023. "Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:933-:d:1024985
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/933/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/933/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
    2. Tongtiegang Zhao & Jianshi Zhao & Xiaohui Lei & Xu Wang & Bisheng Wu, 2017. "Improved Dynamic Programming for Reservoir Flood Control Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2047-2063, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zéphyr, Luckny & Lang, Pascal & Lamond, Bernard F. & Côté, Pascal, 2017. "Approximate stochastic dynamic programming for hydroelectric production planning," European Journal of Operational Research, Elsevier, vol. 262(2), pages 586-601.
    2. Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
    3. Gaivoronski, Alexei & Sechi, Giovanni M. & Zuddas, Paola, 2012. "Cost/risk balanced management of scarce resources using stochastic programming," European Journal of Operational Research, Elsevier, vol. 216(1), pages 214-224.
    4. Zhong-kai Feng & Wen-jing Niu & Zhi-qiang Jiang & Hui Qin & Zhen-guo Song, 2020. "Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2029-2041, April.
    5. Dias, Bruno Henriques & Tomim, Marcelo Aroca & Marcato, André Luís Marques & Ramos, Tales Pulinho & Brandi, Rafael Bruno S. & Junior, Ivo Chaves da Silva & Filho, João Alberto Passos, 2013. "Parallel computing applied to the stochastic dynamic programming for long term operation planning of hydrothermal power systems," European Journal of Operational Research, Elsevier, vol. 229(1), pages 212-222.
    6. Ariyajunya, Bancha & Chen, Ying & Chen, Victoria C.P. & Kim, Seoung Bum & Rosenberger, Jay, 2021. "Addressing state space multicollinearity in solving an ozone pollution dynamic control problem," European Journal of Operational Research, Elsevier, vol. 289(2), pages 683-695.
    7. Wen-jing Niu & Zhong-kai Feng & Shuai Liu & Yu-bin Chen & Yin-shan Xu & Jun Zhang, 2021. "Multiple Hydropower Reservoirs Operation by Hyperbolic Grey Wolf Optimizer Based on Elitism Selection and Adaptive Mutation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 573-591, January.
    8. Cervellera, Cristiano & Caviglione, Luca, 2009. "Optimization of a peer-to-peer system for efficient content replication," European Journal of Operational Research, Elsevier, vol. 196(2), pages 423-433, July.
    9. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2015. "A multi-step rolled forward chance-constrained model and a proactive dynamic approach for the wheat crop quality control problem," European Journal of Operational Research, Elsevier, vol. 246(2), pages 631-640.
    10. Aili Xie & Pan Liu & Shenglian Guo & Xiaoqi Zhang & Hao Jiang & Guang Yang, 2018. "Optimal Design of Seasonal Flood Limited Water Levels by Jointing Operation of the Reservoir and Floodplains," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 179-193, January.
    11. Huiyuan Fan & Prashant K. Tarun & Victoria C. P. Chen & Dachuan T. Shih & Jay M. Rosenberger & Seoung Bum Kim & Robert A. Horton, 2018. "Data-driven optimization for Dallas Fort Worth International Airport deicing activities," Annals of Operations Research, Springer, vol. 263(1), pages 361-384, April.
    12. Hai-tao Chen & Wen-chuan Wang & Kwok-wing Chau & Lei Xu & Ji He, 2021. "Flood Control Operation of Reservoir Group Using Yin-Yang Firefly Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5325-5345, December.
    13. Cervellera, Cristiano, 2023. "Optimized ensemble value function approximation for dynamic programming," European Journal of Operational Research, Elsevier, vol. 309(2), pages 719-730.
    14. D. Fu & Y. Li & G. Huang, 2013. "A Factorial-based Dynamic Analysis Method for Reservoir Operation Under Fuzzy-stochastic Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4591-4610, October.
    15. Xinyu Wu & Shuai Yin & Chuntian Cheng & Zhiyong Chen & Huaying Su, 2023. "SSDP Model with Inflow Clustering for Hydropower System Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1109-1123, February.
    16. Cervellera, C. & Macciò, D., 2011. "A comparison of global and semi-local approximation in T-stage stochastic optimization," European Journal of Operational Research, Elsevier, vol. 208(2), pages 109-118, January.
    17. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    18. Gauvin, Charles & Delage, Erick & Gendreau, Michel, 2018. "A stochastic program with time series and affine decision rules for the reservoir management problem," European Journal of Operational Research, Elsevier, vol. 267(2), pages 716-732.
    19. A. Alessandri & C. Cervellera & M. Sanguineti, 2007. "Functional Optimal Estimation Problems and Their Solution by Nonlinear Approximation Schemes," Journal of Optimization Theory and Applications, Springer, vol. 134(3), pages 445-466, September.
    20. Yong-Gun Kim & Myong-Bong Jo & Pyol Kim & Song-Nam Oh & Chung-Hyok Paek & Sung-Ryol So, 2021. "Effective Optimization-Simulation Model for Flood Control of Cascade Barrage Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 135-157, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:933-:d:1024985. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.