grey wolf optimizer
TRANSCRIPT
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Scientific Research Group in Egypt (SRGE)
Grey wolf optimizer algorithm
Dr. Ahmed Fouad AliSuez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt .
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LOGO Outline
1.Grey wolf optimizer (GWO) (History and main idea)
3. Grey wolf encircling prey
7. GWO algorithm
2. Social hierarchy of grey wolf
6. Search for prey (exploration)
4. Grey wolf Hunting
5. Attacking prey (exploitation)
8. References
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LOGO Grey wolf optimizer (GWO)(History and main idea)
• Grey wolf optimizer (GWO) is a populationbased meta-heuristics algorithm simulates theleadership hierarchy and hunting mechanismof gray wolves in nature proposed by Mirjaliliet al. in 2014
•Grey wolves are considered as apex predators,which they are at the top of the food chain.
• Grey wolves prefer to live in a groups (packs),each group contains 5-12 members on average.
• All the members in the group have a verystrict social dominant hierarchy as shown inthe following figure.
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LOGO Grey wolf optimizer (GWO)(History and main idea)
• The social hierarchy consists of four levels asfollow.
•The first level is called Alpha (𝛼). The alphawolves are the leaders of the pack and they area male and a female.
•They are responsible for making decisionsabout hunting, time to walk, sleeping place andso on.
•The pack members have to dictate the alphadecisions and they acknowledge the alpha byholding their tails down.
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LOGO Grey wolf optimizer (GWO)(History and main idea)
• The alpha wolf is considered the dominantwolf in the pack and all his/her orders shouldbe followed by the pack members.
Social hierarchy of grey wolf
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LOGO Grey wolf optimizer (GWO)(History and main idea)
•The second level is called Beta (𝛽).
•The betas are subordinate wolves, which helpthe alpha in decision making.
•The beta wolf can be either male or female andit consider the best candidate to be the alphawhen the alpha passes away or becomes veryold.
•The beta reinforce the alpha's commandsthroughout the pack and gives the feedback toalpha.
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LOGO Grey wolf optimizer (GWO)(History and main idea)
• The third level is called Delta (𝛿)
• The delta wolves are not alpha or beta wolvesand they are called subordinates.
•Delta wolves have to submit to the alpha andbeta but they dominate the omega (the lowestlevel in wolves social hierarchy).
•There are different categories of delta asfollows
Scouts. The scout wolves are responsible forwatching the boundaries of the territory andwarning the pack in case of any danger.
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LOGO Grey wolf optimizer (GWO)(History and main idea)
Sentinels:- The sentinel wolves areresponsible for protecting the pack.
Elders:- The elder wolves are theexperienced wolves who used to be alpha orbeta.
Hunters:- The hunters wolves are responsiblefor helping the alpha and beta wolves inhunting and providing food for the pack.
Caretakers:- The caretakers are responsiblefor caring for the ill, weak and woundedwolves in the pack.
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LOGO Grey wolf optimizer (GWO)(History and main idea)
The fourth (lowest) level is called Omega (𝜔)
•The omega wolves are considered thescapegoat in the pack, they have to submit toall the other dominant wolves.
•They may seem are not important individualsin the pack and they are the last allowedwolves to eat.
•The whole pack are fighting in case of losingthe omega.
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LOGO Social hierarchy of grey wolf
• In the grey wolf optimizer (GWO), weconsider the fittest solution as the alpha , andthe second and the third fittest solutions arenamed beta and delta , respectively.
•The rest of the solutions are considered omega
•In GWO algorithm, the hunting is guided by 𝛼𝛽 and 𝛿
• The 𝜔 solutions follow these three wolves.
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LOGO Grey wolf encircling prey
•During the hunting, the grey wolves encircleprey.
•The mathematical model of the encirclingbehavior is presented in the followingequations.
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LOGO Grey wolf encircling prey (Cont.)
Where t is the current iteration, A and C arecoefficient vectors, Xp is the position vector ofthe prey, and X indicates the position vector ofa grey wolf.
•The vectors A and C are calculated as follows:
Where components of a are linearly decreased from 2 to 0 over the course of iterations and r1, r2 are random vectors in [0, 1]
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LOGO Grey wolf Hunting
•The hunting operation is usually guided bythe alpha .
•The beta and delta might participate inhunting occasionally.
•In the mathematical model of huntingbehavior of grey wolves, we assumed the alpha, beta and delta have better knowledge aboutthe potential location of prey.
•The first three best solutions are saved and theother agent are oblige to update their positionsaccording to the position of the best searchagents as shown in the following equations.
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LOGO Grey wolf Hunting
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LOGO Attacking prey (exploitation)
•The grey wolf finish the hunt by attacking theprey when it stop moving.
•The vector A is a random value in interval[-2a, 2a], where a is decreased from 2 to 0 overthe course of iterations.
When |A| < 1, the wolves attack towards theprey, which represents an exploitation process.
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LOGO Search for prey (exploration)
•The exploration process in GWO is appliedaccording to the position , and , that divergefrom each other to search for prey andconverge to attack prey.
•The exploration process modeledmathematically by utilizing A with randomvalues greater than 1 or less than -1 to obligethe search agent to diverge from the prey.
When |A| > 1, the wolves are forced todiverge from the prey to fined a fitter prey.
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LOGO GWO algorithmParameters initialization
Population initialization
Assign the best three solutions
Solutions updating
Termination criteria
Produce the best solution
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LOGO References
S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey WolfOptimizer," Advances in Engineering Software, vol. 69, pp.46-61, 2014.
