Grey Wolf Optimizer Algorithm (GWO)
The GWO algorithm is based on mimics the leadership hierarchy and
hunting mechanism of grey wolves in nature and developed by Mirjalili et
al [17]. In the hierarchy of GWO four types of members (search
agents) can be considered: alpha (α ), beta (β ), delta
(δ ), and omega (ω ). The dominance gradually decreases fromα wolves to ω wolves.
The mathematical model of hunting mechanism of grey wolves consists of
the three steps, such as searching for prey, encircling prey, and
attacking prey [17]:
(37)
(38)
(39)
(40)
(41)
where t is the current iteration, A and C are
coefficient vectors, u p is the position
vector of prey, u is the position vector of a grey wolf
(α , β and δ ), a is vector which components
are lineary decreased from 2 to 0 over the course of iterations,r 1 and r 2 are random
vectors in [0,1]. The search agents ω update their positions
according to the position of three best search agents. The grey wolves
finish the hunt by attacking the prey when it stops moving. This can be
mathematically modeled by decreasing the value of vector a from
2 to 0 with iterations.