Improved Grey Wolf Optimizer (I-GWO)

版本 1.0.0 (145.6 KB) 作者: Seyedali Mirjalili
One of the best improvement of the Grey Wolf Optimizer
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更新时间 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

引用格式

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. 检索来源 .

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I-GWO

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1.0.0