Improved Grey Wolf Optimizer (I-GWO)

One of the best improvement of the Grey Wolf Optimizer
Updated 16 Oct 2020

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The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy can enhance the balance between local and global search and maintains diversity.

Author and programmer: M. H. Nadimi-Shahraki, S. Taghian, S. Mirjalili e-Mail:,,

Main paper: M. H. Nadimi-Shahraki, S. Taghian, S. Mirjalili, An Improved Grey Wolf Optimizer for Solving, Engineering Problems, Expert Systems with Applications, in press, DOI: 10.1016/j.eswa.2020.113917

Cite As

Seyedali Mirjalili (2024). Improved Grey Wolf Optimizer (I-GWO) (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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