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:/matlabcentral/fileexchange/47258-grey-wolf-optimizer-toolbox
Other relevant submissions: https://au.bat365/matlabcentral/fileexchange/49772-grey-wolf-optimizer-for-training-multi-layer-perceptrons
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