IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i7d10.1007_s11269-022-03141-0.html
   My bibliography  Save this article

Application of Swarm Intelligence and Evolutionary Computation Algorithms for Optimal Reservoir Operation

Author

Listed:
  • Arya Yaghoubzadeh-Bavandpour

    (Bu-Ali Sina University)

  • Omid Bozorg-Haddad

    (University of California
    University of Tehran)

  • Mohammadreza Rajabi

    (University of Tehran)

  • Babak Zolghadr-Asli

    (University of Tehran)

  • Xuefeng Chu

    (North Dakota State University)

Abstract

Real-world problems often contain complex structures and various variables, and classical optimization techniques may face difficulties finding optimal solutions. Hence, it is essential to develop efficient and robust techniques to solve these problems. Computational intelligence (CI) optimization methods, such as swarm intelligence (SI) and evolutionary computation (EC), are promising alternatives to conventional gradient-based optimizations. SI algorithms are multi-agent systems inspired by the collective behavior of individuals, while EC algorithms implement adaptive search inspired by the evolution process. This study aims to compare SI and EC algorithms and to compare nature-based and human-based algorithms in the context of water resources planning and management to optimize reservoir operation. In this study four optimization algorithms, including particle swarm optimization (PSO), teaching–learning based optimization algorithm (TLBO), genetic algorithm (GA), and cultural algorithm (CA), were applied to determine the optimal operation of the Aydoghmoush reservoir in Iran. This study used four criteria, known as objective function value, run time, robustness, and convergence rate, to compare the overall performances of the selected optimization algorithms. In term of the objective function, PSO, TLBO, GA, and CA achieved 2.81 × 10–31, 1.66 × 10–24, 4.29 × 10–4, and 1.44 × 10–2, respectively. The results suggested that although both SI and EC algorithms performed acceptably and provided optimal solutions for reservoir operation, SI algorithms outperformed the EC algorithms in terms of accuracy of solutions, convergence rate, and run time to reach global optima.

Suggested Citation

  • Arya Yaghoubzadeh-Bavandpour & Omid Bozorg-Haddad & Mohammadreza Rajabi & Babak Zolghadr-Asli & Xuefeng Chu, 2022. "Application of Swarm Intelligence and Evolutionary Computation Algorithms for Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2275-2292, May.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:7:d:10.1007_s11269-022-03141-0
    DOI: 10.1007/s11269-022-03141-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03141-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-022-03141-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. B. Sriman Pankaj & M. Naveen Naidu & A. Vasan & Murari RR Varma, 2020. "Self-Adaptive Cuckoo Search Algorithm for Optimal Design of Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3129-3146, August.
    2. Rosalva Mendoza Ramírez & Maritza Liliana Arganis Juárez & Ramón Domínguez Mora & Luis Daniel Padilla Morales & Óscar Arturo Fuentes Mariles & Alejandro Mendoza Reséndiz & Eliseo Carrizosa Elizondo & , 2021. "Operation Policies through Dynamic Programming and Genetic Algorithms, for a Reservoir with Irrigation and Water Supply Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1573-1586, March.
    3. Omid Haddad & Abbas Afshar & Miguel Mariño, 2006. "Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 661-680, October.
    4. Yufei Ma & Ping-an Zhong & Bin Xu & Feilin Zhu & Yao Xiao & Qingwen Lu, 2020. "Multidimensional Parallel Dynamic Programming Algorithm Based on Spark for Large-Scale Hydropower Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3427-3444, September.
    5. Yongqi Liu & Hui Qin & Li Mo & Yongqiang Wang & Duan Chen & Shusen Pang & Xingli Yin, 2019. "Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 337-354, January.
    6. Junfei Chen & Cong Yu & Miao Cai & Huimin Wang & Pei Zhou, 2020. "Multi-Objective Optimal Allocation of Urban Water Resources While Considering Conflict Resolution Based on the PSO Algorithm: A Case Study of Kunming, China," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    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. Mohammad Hassan Salmani & Kourosh Eshghi, 2017. "A Metaheuristic Algorithm Based on Chemotherapy Science: CSA," Journal of Optimization, Hindawi, vol. 2017, pages 1-13, February.
    2. Ijaz Ahmad & Fan Zhang, 2022. "Optimal Agricultural Water Allocation for the Sustainable Development of Surface and Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4219-4236, September.
    3. Zitong Yang & Xianfeng Huang & Jiao Liu & Guohua Fang, 2021. "Optimal Operation of Floodwater Resources Utilization of Lakes in South-to-North Water Transfer Eastern Route Project," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    4. Zhao, Hongye & Liao, Shengli & Fang, Zhou & Liu, Benxi & Ma, Xiangyu & Lu, Jia, 2024. "Short-term peak-shaving operation of “N-reservoirs and multicascade” large-scale hydropower systems based on a decomposition-iteration strategy," Energy, Elsevier, vol. 288(C).
    5. Meraj Sohrabi & Zeynab Banoo Ahani Amineh & Mohammad Hossein Niksokhan & Hossein Zanjanian, 2023. "A framework for optimal water allocation considering water value, strategic management and conflict resolution," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1582-1613, February.
    6. A. Dariane & S. Sarani, 2013. "Application of Intelligent Water Drops Algorithm in Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4827-4843, November.
    7. Yongqi Liu & Lei Ye & Hui Qin & Shuo Ouyang & Zhendong Zhang & Jianzhong Zhou, 2019. "Middle and Long-Term Runoff Probabilistic Forecasting Based on Gaussian Mixture Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1785-1799, March.
    8. Chen Wang & Yizi Shang & Majid Khayatnezhad, 2021. "Fuzzy Stress-based Modeling for Probabilistic Irrigation Planning Using Copula-NSPSO," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4943-4959, November.
    9. Omid Bozorg Haddad & Abbas Afshar & Miguel Mariño, 2008. "Design-Operation of Multi-Hydropower Reservoirs: HBMO Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(12), pages 1709-1722, December.
    10. Omid Bozorg-Haddad & Mahboubeh Zarezadeh-Mehrizi & Mehri Abdi-Dehkordi & Hugo A. Loáiciga & Miguel A. Mariño, 2016. "A self-tuning ANN model for simulation and forecasting of surface flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2907-2929, July.
    11. Mohammad Solgi & Omid Bozorg-Haddad & Hugo A. Loáiciga, 2017. "The Enhanced Honey-Bee Mating Optimization Algorithm for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 885-901, February.
    12. Mohammad Azizipour & Vahid Ghalenoei & M. H. Afshar & S. S. Solis, 2016. "Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3995-4009, September.
    13. Wen-jing Niu & Zhong-kai Feng & Yu-rong Li & Shuai Liu, 2021. "Cooperation Search Algorithm for Power Generation Production Operation Optimization of Cascade Hydropower Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2465-2485, June.
    14. Kaveh, Mehrdad & Mesgari, Mohammad Saadi & Saeidian, Bahram, 2023. "Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 95-135.
    15. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    16. Iman Ahmadianfar & Saeed Noshadian & Nadir Ahmed Elagib & Meysam Salarijazi, 2021. "Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3513-3538, September.
    17. Mohammed Falah Allawi & Othman Jaafar & Mohammad Ehteram & Firdaus Mohamad Hamzah & Ahmed El-Shafie, 2018. "Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3373-3389, August.
    18. Iman Ahmadianfar & Arvin Samadi-Koucheksaraee & Omid Bozorg-Haddad, 2017. "Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4375-4397, November.
    19. Shengli Liao & Yan Zhang & Jie Liu & Benxi Liu & Zhanwei Liu, 2021. "Short-Term Peak-Shaving Operation of Single-Reservoir and Multicascade Hydropower Plants Serving Multiple Power Grids," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 689-705, January.
    20. Songphol Songsaengrit & Anongrit Kangrang, 2022. "Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization," Sustainability, MDPI, vol. 14(14), pages 1-17, July.

    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:spr:waterr:v:36:y:2022:i:7:d:10.1007_s11269-022-03141-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.