Gray wolf Optimization Algorithm (GWO) Step-By-Step Explanation with Exa...

Grey Wolf Optimization algorithm is a metaheuristic proposed by Mirjaliali Mohammad and Lewis, 2014.GWO is inspired by the social hierarchy and the hunting technique of Grey Wolves.Grey Wolves Encircle the prey during hunting. 

Gray wolf Optimization Algorithm (GWO) Step-By-Step Explanation with Example (Part - 2)~xRay Pixy
Topics covered in this video

      What is Grey wolf Algorithm?

How to Initialize Grey Wolf Population?

How to Calculate Fitness value for each wolf?

How to Update Grey Wolf Position?

How to Calculate Alpha, Beta, Delta Wolf Score?

Grey Wolf Hunting

Hunting process is guided by Alpha.  It is assumed that α, β, δ have better knowledge about the location of prey (i.e., the optimal solution). Other wolves will update their positions according to the position of α, β, δ. When Prey stop moving wolves attack it to finish hunting process. This is modeled by decreasing 𝑎 ⃗ from 2 to 0 during the iterations. As 𝑎 ⃗ decrease 𝐴 ⃗ also decreases. A <1 Forces the wolf to attack toward the prey.

Grey Wolf Optimization algorithm Steps:

1.) Initialize Grey Wolf Population.

2.) Initialize a, A and C.

3.) Calculate the fitness of each search agent.

4.) 𝑿_𝜶 = best search agent

5.) 𝑿_𝜷 = second best search agent

6.) 𝑿_𝜹 = third best search agent.

7.) while (t<Max number of iteration)

 8.) For each search agent 

     update the position of current search agent by above equations

end for

9.) update a, A and C

10.) Calculate the fitness of all search agent.

11.) update 𝑿_𝜶, 𝑿_𝜷, 𝑿_𝜹

12.) t = t+1

end while

13.) return 𝑿_𝜶

Grey Wolf Optimization Algorithm Flowchart