Grey Wolf Optimizer
What is this algorithm?
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.
Single-objective 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.
Multi-objective optimization
If you are interested in solving a multi-objective problem using GWO, you have to use this code.
Matlab GWO toolbox
A user-friendly interface to run GWO algorithm with minimum coding.
My Improved GWO Optimizer
AGWO: Advanced GWO in multi-layer perception optimization [CODE]
DSGWO: An improved grey wolf optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms [CODE]
EGWO: Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement [CODE]
GWO in Different Programming Languages
