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.