The Grey Wolf Optimizer (GWO) 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.
GWO has been designed to solve single-objective optimization problems. This algorithm has been implemented in a wide range of programming languages. You can download the source code at the bottom of this page.
If you are interested in solving a multi-objective problem using GWO, you have to use this code.
A user-friendly interface to run GWO algorithm with minimum coding.
One of the best improvements of GWO called I-GWO (Credits to Dr. Mohammad H. Nadimi-Shahraki).
Watch this video to learn about the inspirations for the GWO algorithm.
Watch this video to learn how and what I have designed the mathematical equatiosn for the GWO algorithm.
Watch this video to learn the role of the main controlling parameters in this algorithm.