is a nature-inspired, metaheuristic optimization algorithm based on the social hierarchy and hunting behavior of grey wolves in the wild. Introduced by Mirjalili et al. in 2014, GWO is used to solve optimization problems in various fields, such as engineering, machine learning, and operational research.
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Social Hierarchy: The algorithm mimics the social structure of a wolf pack, which is categorized into four types of wolves:
- Alpha ⋉: The leader, representing the best solution.
- Beta ₿: The second-best solution, assisting the alpha.
- Delta ₷: The third-best solution, subordinate to alpha and beta.
- Omega w : The remaining wolves, representing the rest of the potential solutions.
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Hunting Behavior: GWO simulates the cooperative hunting strategy of grey wolves. The three main phases include:
- Encircling Prey: Wolves surround the prey by updating their positions based on the location of alpha, beta, and delta wolves.
- Hunting: Wolves update their positions towards the prey by following the leaders.
- Attacking the Prey: As the wolves converge on the prey, the search space is reduced, leading to exploitation.
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Mathematical Modeling: The position of wolves is updated using equations that model the encircling and hunting behavior, adjusting their movements based on the distance from the alpha, beta, and delta wolves.
- Simple Implementation**: GWO has a straightforward structure and is easy to implement.
- Fewer Parameters**: It requires fewer parameters to adjust compared to other algorithms.
- Good Exploration and Exploitation**: Effectively balances exploration (searching globally) and exploitation (refining the best solutions).
GWO is widely used for solving optimization problems due to its simplicity and effectiveness in finding high-quality solutions in complex, multi-dimensional search spaces.