Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow

Wang, Lili; Guo, Yong; Fan, Manhong

doi:10.1007/s11269-022-03262-6

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…In order to better simulate the hunting behavior of gray wolves, the three gray wolves with the best fitness values are retained in each iteration of GWO algorithm, and the position information of W wolf is updated by combining the position information of Wolf  , Wolf  and Wolf  . The mathematical model of hunting behavior of gray wolves is as follows: ( 5)-( 7) [7] .…”

Section: 1gwo Algorithm (1) the Principle Of Gwo Algorithmmentioning

confidence: 99%

“…, , of Wolf  , Wolf  and Wolf  are saved. Secondly, modifying the function of wolf W using models ( 5) to (7), modifying the parameters of the updated function using models ( 3) and ( 4), calculating the fitness of each individual, comparing them with the fitness of the previous methods, and select three gray wolves with the best value of exercise to continue searching for the prey. Finally, determine whether the iteration has reached its maximum value.…”

Section: 1gwo Algorithm (1) the Principle Of Gwo Algorithmmentioning

confidence: 99%

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Application of Improved GWO Algorithm in Course Scheduling System of University Teaching Management

Zhang

2024

Proceedings of the 3rd International Conference on New Media Development and Modernized Education, NMDME 2023, October 13–15, 2

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The expansion of the scale of running a school in colleges and universities makes the course arrangement in colleges and universities face great challenges. Therefore, the improved GWO algorithm is used to study the process of setting up the algorithm in the college teaching management. Analyze the principle and process of GWO algorithm. On this basis, using chaos theory, the first gray wolf population of GWO algorithm is constructed using Chebyshev chaotic sequence, and Levi flight is used to improve the gray wolf position adjustment formula, which makes an improved GWO algorithm. Through simulation optimization for some school's teaching process, it is verified that the improved GWO algorithm avoids the algorithm from falling into the local optimization and achieves good results in setting up the curriculum resources. This study has certain value for optimizing the teaching process.

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Section: 1gwo Algorithm (1) the Principle Of Gwo Algorithmmentioning

confidence: 99%

Section: 1gwo Algorithm (1) the Principle Of Gwo Algorithmmentioning

confidence: 99%