Improved Grey Wolf Optimizer (I-GWO)

バージョン 1.0.0 (146 KB) 作成者: Seyedali Mirjalili
One of the best improvement of the Grey Wolf Optimizer
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更新 2020/10/16

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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: nadimi@ieee.org, shokooh.taghian94@gmail.com, ali.mirjalili@gmail.com

http://www.alimirjalili.com

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

引用

Seyedali Mirjalili (2024). Improved Grey Wolf Optimizer (I-GWO) (https://www.mathworks.com/matlabcentral/fileexchange/81253-improved-grey-wolf-optimizer-i-gwo), MATLAB Central File Exchange. 取得済み .

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I-GWO

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1.0.0