Grey Wolf Optimizer (GWO) is inspired by grey wolves. The leadership hierarchy and hunting mechanism of grey wolves is mimicked by GWO algorithm. GWO employs four types of grey wolves such as alpha, beta, delta, and omega. GWO consists of three main steps of hunting, searching for prey. Encircling prey, and attacking prey [38].
GWO is benchmarked on many challenging test problems and applied to some real engineering problems. The results reveal that GWO is able to provide very competitive results compared to these well-known meta-heuristics.
Meta-heuristics algorithms have become remarkably common due to some reasons: simplicity, flexibility, local optima avoidance, and derivative free mechanism. The simplicity of these algorithms originates …show more content…
Alpha is responsible for making decisions about hunting, sleeping place, time to wake. The alpha’s decisions are dictated to the pack. They are called the dominant wolf since their orders should be followed by the pack. They are only allowed to mate in the pack. Alpha is not necessarily the strongest member of the pack, but the best in term of managing the pack [38].
The betas are subordinate wolves, they help the alpha in decision making and other activities .They may be either male or female. They are probably the best candidate to be the alpha. They play the role of an advisor to the alpha and discipliner for the pack [38].
Omegas are the lowest ranking grey wolf. They always have to submit to all the other dominant wolves. In the case of losing the omega, the whole pack face internal fighting and problems.
Deltas have to submit to alpha and beta, but they dominate the omega. This type consists of five groups. They are scouts, sentinels, elders, hunters and caretakers. Scouts are responsible for warning the pack in case of any danger. Sentinels guarantee the safeties of the pack. Elders are the experienced wolves. Hunters provide food for the pack. Finally, the caretakers are responsible for caring for the weak, ill wolves in the pack .Hierarchy of grey wolf is shown in Fig. 3.1.
GWO is benchmarked on many challenging test problems and applied to some real engineering problems. The results reveal that GWO is able to provide very competitive results compared to these well-known meta-heuristics.
Meta-heuristics algorithms have become remarkably common due to some reasons: simplicity, flexibility, local optima avoidance, and derivative free mechanism. The simplicity of these algorithms originates …show more content…
Alpha is responsible for making decisions about hunting, sleeping place, time to wake. The alpha’s decisions are dictated to the pack. They are called the dominant wolf since their orders should be followed by the pack. They are only allowed to mate in the pack. Alpha is not necessarily the strongest member of the pack, but the best in term of managing the pack [38].
The betas are subordinate wolves, they help the alpha in decision making and other activities .They may be either male or female. They are probably the best candidate to be the alpha. They play the role of an advisor to the alpha and discipliner for the pack [38].
Omegas are the lowest ranking grey wolf. They always have to submit to all the other dominant wolves. In the case of losing the omega, the whole pack face internal fighting and problems.
Deltas have to submit to alpha and beta, but they dominate the omega. This type consists of five groups. They are scouts, sentinels, elders, hunters and caretakers. Scouts are responsible for warning the pack in case of any danger. Sentinels guarantee the safeties of the pack. Elders are the experienced wolves. Hunters provide food for the pack. Finally, the caretakers are responsible for caring for the weak, ill wolves in the pack .Hierarchy of grey wolf is shown in Fig. 3.1.