Grey Wolf Optimization (GWO) is a meta-heuristic swarm intelligence (SI)
optimization algorithm, which uses the collective intelligence of a group of
simple agents to solve nature related problems. The GWO mimics the social
leadership hierarchy and hunting behaviour of grey wolves. Grey Wolf
Optimization (GWO) is first discussed in [Mirjalili S, 2014]. Several studies
have presented competitive results for GWO in terms of improved convergence
and avoidance of local optima values compared to the other popular known SI
techniques such as, the birds flocking behaviour based Particle Swarm
Optimization (PSO) [James Kenned], Genetic Algorithm (GA), the ant
behaviour of finding the shortest path between the nest and food source is used
in an ant colony is used in the Ant Colony Optimization (ACO) [Marco Dorigo,
] and Artificial Bee Colony (ABC) optimization algorithm based on the
collective behaviour of bees searching food. The GWO algorithm is applicable
in real world challenging problems with unknown search spaces as the
optimization initiates using random solutions without the need for computing
the costly derivatives of the search spaces, incurring computational burden.
Handling the optimization problem stochastically enables GWO to presents
superior ability to avoid local optima compared to the conventional models.
Easy implementation with fewer operators and parameters, the iteration details
of the search space is preserved to be used.
Grey Wolf Optimization (GWO): Background
The grey wolfs lives in pack and the hunting process is a group activity which
follows strict dominant hierarchy that reduces down the hierarchy for the
hunting based on the leadership features, is mimicked in the GWO algorithm.
The main phases of grey wolf hunting are:
 Tracking,
 Chasing, and approaching the prey.
 Pursuing, encircling, and harassing the prey until it stops moving.
 Attack towards the prey.