GWO (Gray Wolf Optimization)

Information

• The GWO (Gray Wolf Optimization) algorithm is an optimization algorithm inspired by the social hunting behavior of grey wolves in the wild.
• The algorithm involves a population of candidate solutions (or "wolves") that are initialized randomly in the search space.
• Each wolf in the population represents a potential solution to the optimization problem being solved.
• The algorithm iteratively updates the position of the wolves in the search space based on their fitness, which is evaluated using an objective function.
• The position of each wolf is updated based on three types of movements: (i) an alpha wolf (the best solution found so far) leads the pack and moves towards the best area of the search space, (ii) beta and delta wolves follow the alpha and move in its direction, and (iii) the rest of the wolves move randomly in the search space.
• The search process continues for a fixed number of iterations, or until a stopping criterion is met (e.g., the maximum number of iterations is reached or the improvement in the objective function is below a certain threshold).
• GWO has been shown to be effective for a range of optimization problems, including those with high-dimensional search spaces, non-convex and multimodal objective functions, and noisy or stochastic evaluation functions.
• However, GWO has some limitations, such as being sensitive to parameter settings and requiring a large number of function evaluations in some cases.