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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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    Cited by:

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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).
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