Grey wolf optimizer: Theory, literature review, and application in computational fluid dynamics problems
File version
Author(s)
Aljarah, I
Mafarja, M
Heidari, AA
Faris, H
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Mirjalili, Seyedali
Dong, Jin Song
Lewis, Andrew
Date
Size
File type(s)
Location
License
Abstract
This chapter first discusses inspirations, methematicam models, and an in-depth literature of the recently proposed Grey Wolf Optimizer (GWO). Then, several experiments are conducted to analyze and benchmark the performance of different variants and improvements of this algorithm. The chapter also investigates the application of the GWO variants in finding an optimal design for a ship propeller.
Journal Title
Conference Title
Book Title
Nature-Inspired Optimizers: Theories, Literature Reviews and Applications
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Artificial intelligence
Control engineering, mechatronics and robotics
Machine learning
Persistent link to this record
Citation
Mirjalili, S; Aljarah, I; Mafarja, M; Heidari, AA; Faris, H, Grey wolf optimizer: Theory, literature review, and application in computational fluid dynamics problems, Nature-Inspired Optimizers: Theories, Literature Reviews and Applications, 2020, pp. 87-105