Improved Krill Algorithm for Reservoir Operation

Hojat Karami (), Sayed Farhad Mousavi, Saeed Farzin, Mohammad Ehteram, Vijay P. Singh and Ozgur Kisi
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 10, No 7, 3353-3372

Abstract: 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.

Keywords: Krill algorithm; Genetic algorithm; Particle swarm algorithm; Reservoir operation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11269-018-1995-4

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