The Grey Wolf Optimizer(GWO) algorithm 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.

Matlab source codes of GWO are available here Java source codes of GWO are available here Python source codes of GWO are available here Ruby source codes of GWO are available here Visual Basic source codes of GWO are available here

Good news: GWO is now the most cited paper of the ADES journal: http://www.journals.elsevier.com/advances-in-engineering-software/most-cited-articles

In the Multi-Objective Grey Wolf Optimizer (MOGWO), a fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive has been employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces.

Source Codes of MOGWO are available here