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Improved Krill Algorithm for Reservoir Operation

Author

Listed:
  • Hojat Karami

    (Semnan University)

  • Sayed Farhad Mousavi

    (Semnan University)

  • Saeed Farzin

    (Semnan University)

  • Mohammad Ehteram

    (Semnan University)

  • Vijay P. Singh

    (Texas A&M University)

  • Ozgur Kisi

    (Ilia State University)

Abstract

Much of the world is facing water scarcity during one or the other part of the year. Hence, water resources management and optimal operation of water resources system take on added importance these days. This study introduces an improved version of krill algorithm for reservoir operation. The algorithm is based on adding an onlooker search mechanism to avoid being trapped in local optima and then updating its position. The new krill algorithm is tested using a case study for irrigation management. The computation time is 33 s for the new algorithm but is 54, 59, and 60 s for krill algorithm, particle swarm optimization and genetic algorithm, respectively. Also, the improved krill algorithm can meet 97% of irrigation demands and has the lowest value of vulnerability index among genetic algorithm, particle swarm optimization, and simple krill algorithm. Also, the average solution of improved krill algorithm is close to the global solution. Results indicate that the improved krill algorithm has high potential for application in water resource management.

Suggested Citation

  • Hojat Karami & Sayed Farhad Mousavi & Saeed Farzin & Mohammad Ehteram & Vijay P. Singh & Ozgur Kisi, 2018. "Improved Krill Algorithm for Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3353-3372, August.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:10:d:10.1007_s11269-018-1995-4
    DOI: 10.1007/s11269-018-1995-4
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    References listed on IDEAS

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    2. Almubaidin, Mohammad Abdullah Abid & Ahmed, Ali Najah & Malek, Marlinda Abdul & Mahmoud, Moamin A. & Sherif, Mohsen & El-Shafie, Ahmed, 2024. "Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios," Agricultural Water Management, Elsevier, vol. 293(C).
    3. Guang Yang & Shenglian Guo & Pan Liu & Xiaofeng Liu & Jiabo Yin, 2020. "Heuristic Input Variable Selection in Multi-Objective Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 617-636, January.
    4. Zaher Mundher Yaseen & Mohammad Ehteram & Md. Shabbir Hossain & Chow Ming Fai & Suhana Binti Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Jaafar & Haitham Abdulmohsin Afan & Lai Sai Hin & Nuratiah, 2019. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems," Sustainability, MDPI, vol. 11(7), pages 1-28, April.
    5. Iman Ahmadianfar & Bijay Halder & Salim Heddam & Leonardo Goliatt & Mou Leong Tan & Zulfaqar Sa’adi & Zainab Al-Khafaji & Raad Z. Homod & Tarik A. Rashid & Zaher Mundher Yaseen, 2023. "An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems," Sustainability, MDPI, vol. 15(3), pages 1-28, January.
    6. Mahdi Valikhan-Anaraki & Sayed-Farhad Mousavi & Saeed Farzin & Hojat Karami & Mohammad Ehteram & Ozgur Kisi & Chow Ming Fai & Md. Shabbir Hossain & Gasim Hayder & Ali Najah Ahmed & Amr H. El-Shafie & , 2019. "Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
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