Grey Wolf Optimisation Case Study

Grey Wolf Optimisation (GWO) is a new population-based metaheuristic algorithm based on the ways of living and hunting behavior of wolves []. In this technique, a specific role is defined for every member of group. One of the interesting facts about the life of wolves is their hierarchy structure in the group. In fact, there is a special types of wolf named alphas (β) that are leader, can be a male or female. As leader, the alphas can make decision about hunting, sleeping place and time to wake of other wolves (this decision-making process called pack). The second level in the grey wolves’ hierarchy is beta (β). The betas are those which help alphas to carry out their pack activities and convey group members’ opinions to alphas, so that
they
are as an advisor to the alphas, and all wolves in a group except alphas should respect them. Delta (δ) is the third level in wolves group which should respect to alphas and betas, but all other lower-level wolves should respect them. Delta has some responsibilities such as inspecting borders of territory, protecting the group and hunting. The lowest ranking grey wolves are omegas
(ω). Omega wolves always have to respect the higher-level wolves and eat after them.
In the GWO algorithm, each wolf represent a solution and similar to the other swarm intelligence-based approaches, it starts with an random initial population of solutions and continues in an iterative process.