Grey Wolf Optimizer (GWO)

GWO is a novel meta-heuristic algorithm for global optimization

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Updated 22 May 2018

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The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented to perform optimization.
This is the source codes of the paper: S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, Volume 69, March 2014, Pages 46-61, ISSN 0965-9978, http://dx.doi.org/10.1016/j.advengsoft.2013.12.007.
More information can be found in: http://www.alimirjalili.com/GWO.html
You can find the Grey Wolf optimizer Toolbox here: http://www.mathworks.com/matlabcentral/fileexchange/47258-grey-wolf-optimizer-toolbox
Other relevant submissions: https://au.mathworks.com/matlabcentral/fileexchange/49772-grey-wolf-optimizer-for-training-multi-layer-perceptrons
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Cite As

Seyedali Mirjalili (2023). Grey Wolf Optimizer (GWO) (https://www.mathworks.com/matlabcentral/fileexchange/44974-grey-wolf-optimizer-gwo), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2011b
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.6

Links added:
https://www.udemy.com/optimisation/?couponCode=MATHWORKSREF
https://www.udemy.com/geneticalgorithm/?couponCode=MATHWORKSREF
typo
A link has been added to the description

1.5.0.0

This submission is now available as a Toolbox file in R2014b.

1.4.0.0

Typo fixed

1.3.0.0

The paper has been included in the submission.

1.2.0.0

A link to the GWO toolbox was added.

1.1.0.0

An issue in boundary checking was resolved and the source filed have been updated.

1.0.0.0