4.2.4. Grey Wolf Optimization

The GWO technique was proposed in 2014. It is motivated by social stratification and the gray wolf's behavioral hunting personality [51]. Grey wolves, as a whole, live in packs with typical size of around 5–12. According to the hierarchical chain shown in Figure 15, grey wolves are classified into four categories based on their community supremacy. Alpha (α) wolves are the pioneer at the peak and are thus regarded as the best sources of solutions for a given optimization problem. Beta (β) wolves pursue the (α) and assist them in fulfilling their tasks. They take (α) wolves' position if the (α) wolves die. The delta (δ) wolves make up the pack's hunters, keepers, and explorers and are the second end-class. As a result, (β) and (δ) wolves represent the second- and third-best solutions, correspondingly. Omega (ω) wolves are the last group, which make up the youngest members and therefore stand for the residual solution [52].

**Figure 15.** Grey wolves hierarchy sequence.

The supremacy of wolves is reduced as the position of the wolves lowers in the hierarchical order from top to bottom. Aside from the community order of wolves, the grey wolf's social behavior is also heavily influenced by aggregation hunting. On the basis of this, the GWO algorithm's mathematical model analyzes the following measure [52]:

#### **Step-1: Social Hierarchy**

The GWO technique presumes (α) as the fittest solution, followed by (β) and (δ) as the second- and third-finest solutions, to simulate the hierarchical system of wolves. (ω) is thought to represent the left-over contender solutions. Thus α, β and δ wolves guide the hunting process with ω wolves trailing behind.
