Nature-inspired optimisation through hierarchical leadership.

Grey Wolf Optimisation (GWO) is a metaheuristic algorithm inspired by the social hierarchy and cooperative hunting strategies of grey wolves. The algorithm models leadership roles—alpha, beta, delta, and omega—and uses them to guide the search for optimal solutions.

The algorithm simulates three core behaviours:

• Encircling prey — wolves estimate the position of the optimal solution.
• Hunting — alpha, beta, and delta guide the pack.
• Attacking — convergence occurs as wolves close in on the prey.

The project implements the full GWO algorithm, including:

• Random initialisation of wolf positions.
• Fitness evaluation for each wolf.
• Alpha–beta–delta leadership updates.
• Position updates using encircling and hunting equations.
• Convergence tracking and optimisation curves.

The algorithm demonstrates stable convergence on multiple optimisation landscapes, including multimodal and unimodal functions. Its balance between exploration and exploitation allows it to escape local minima effectively.