Research Paper On Grey Wolf
3. Hunting
Grey wolves can recognize the position of prey and encircle them. usually the alpha guides the hunt. The beta and delta may likewise take an interest hunting occasionally. However, in an abstract search space we have no idea about the position of the optimum (prey). For mathematically simulating the hunting behavior of grey wolves, we consider that the α (best candidate solution), β, and δ have better knowledge about the position of prey. Hence we save the first three best solutions got so far and oblige the other search candidates (including the omegas) to update their locations according to the position of the best search agents. The following formulas are proposed = , = , = ……………………………………………….(3.5) , , ……………………………………………….(3.6) …show more content…
Search for prey (exploration)
Grey wolves search for prey according to the position of the alpha,beta, and delta. They diverge in order to search for prey and converge to attack prey. For mathematically model the divergence, we utilize with random values to oblige search agents to diverge from the prey. It emphasizes exploration and allows to search …show more content…
5(b) shows that for the value |A| > 1 ,the grey wolves diverge from the prey to find a fitter prey. Another component of GWO that favors exploration is . As observed in Eq. (3.4), the vector contains random values in [0, 2]. This component provides random weights for prey in order to randomly emphasize (C > 1) or deemphasize (C < 1) the effect of prey in defining the distance in Eq. (3.1). This helps GWO in having more random behavior in optimization, favoring exploration and avoiding local optima. Unlike , C is not linearly decreased .
We can be consider C vector as the effect of obstacles for approaching prey.
The search starts with creating a random population of grey wolves (candidate solutions) in the GWO algorithm. During the iterations, α, β, and δ estimate the probable position of the prey. Then Each candidate solution updates its position from the prey accordingly. The parameter a is decreased from 2 to 0 in order to emphasize exploration and exploitation, respectively. Candidate solutions diverge from the prey if |A| > 1 and converge towards the prey if |A| < 1. Finally, the GWO algorithm is terminated by the satisfaction of an end
Grey wolves can recognize the position of prey and encircle them. usually the alpha guides the hunt. The beta and delta may likewise take an interest hunting occasionally. However, in an abstract search space we have no idea about the position of the optimum (prey). For mathematically simulating the hunting behavior of grey wolves, we consider that the α (best candidate solution), β, and δ have better knowledge about the position of prey. Hence we save the first three best solutions got so far and oblige the other search candidates (including the omegas) to update their locations according to the position of the best search agents. The following formulas are proposed = , = , = ……………………………………………….(3.5) , , ……………………………………………….(3.6) …show more content…
Search for prey (exploration)
Grey wolves search for prey according to the position of the alpha,beta, and delta. They diverge in order to search for prey and converge to attack prey. For mathematically model the divergence, we utilize with random values to oblige search agents to diverge from the prey. It emphasizes exploration and allows to search …show more content…
5(b) shows that for the value |A| > 1 ,the grey wolves diverge from the prey to find a fitter prey. Another component of GWO that favors exploration is . As observed in Eq. (3.4), the vector contains random values in [0, 2]. This component provides random weights for prey in order to randomly emphasize (C > 1) or deemphasize (C < 1) the effect of prey in defining the distance in Eq. (3.1). This helps GWO in having more random behavior in optimization, favoring exploration and avoiding local optima. Unlike , C is not linearly decreased .
We can be consider C vector as the effect of obstacles for approaching prey.
The search starts with creating a random population of grey wolves (candidate solutions) in the GWO algorithm. During the iterations, α, β, and δ estimate the probable position of the prey. Then Each candidate solution updates its position from the prey accordingly. The parameter a is decreased from 2 to 0 in order to emphasize exploration and exploitation, respectively. Candidate solutions diverge from the prey if |A| > 1 and converge towards the prey if |A| < 1. Finally, the GWO algorithm is terminated by the satisfaction of an end