Grey Wolf Optimization
Krishn Kumar Mishra · 2022
Many swarm intelligence–based algorithms have been developed in the past to solve complex optimization problems. These swarm intelligence-based algorithms are classified into two groups. First-class algorithms, such as ant colony optimization and ant–bee colony, use the intelligence of insects to solve optimization problems. Other categories include algorithms that mimic the intelligence of animals and birds, such as partial swarm optimization (PSO), grey wolf optimization (GWO), and many others. Following in the footsteps of PSO, the GWO is gaining popularity as a swarm intelligence algorithm that solves optimization problems by mimicking the hunting process used by grey wolves. Searching, encircling, and attacking processes are used to update the position of each wolf. The position of each wolf is updated under the leadership of alpha, beta, and delta wolves. Parameter vectors A and C play a very important role in defining the convergence of a GWO algorithm.