.png){kind=icon}
Return
Improved Krill Algorithm for Reservoir Operation
.png){kind=icon}
PRE
AI
DOI：10.1007/s11269-018-1995-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.7
Papers:
8.1K
Citations:
1.6W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSaveAortic Function’s Adaptation in Response to Exercise-Induced Stress Assessing by 1.5T MRI: A Pilot Study in Healthy Volunteers.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSave
View more