Grey Wolf Optimizer (GWO) Algorithm

The Grey Wolf Optimizer (GWO) is a metaheuristic optimization algorithm initially proposed by Mirjalili and Mirjalili in 2014. It simulates the hierarchical structure and hunting behavior of grey wolf packs to solve complex optimization problems, demonstrating excellent performance in handling high-dimensional, nonlinear, non-convex, and multimodal optimization challenges. The core concept involves mapping optimization problems to grey wolf hunting behaviors, where individuals interact to search for optimal solutions. The wolf pack hierarchy consists of four roles: Alpha (dominant leader representing the best solution), Beta (second-best solution), Delta (third-best solution), and Omega (remaining solutions). The algorithm updates wolf positions through three main hunting phases: encircling prey, hunting, and attacking, implemented mathematically using position vectors and adaptive parameters. Key implementation components include: - Position initialization using random or Latin Hypercube sampling - Fitness evaluation to determine hierarchy rankings - Adaptive parameter a controlling exploration-to-exploitation transition - Position update equations simulating hunting coordination GWO has been successfully applied in machine learning (feature selection, hyperparameter tuning), image processing (segmentation, enhancement), power system optimization (load dispatch, renewable integration), and logistics (route planning, scheduling). Compared to other metaheuristics like Genetic Algorithms and Ant Colony Optimization, GWO exhibits superior global search capability, faster convergence rates, and fewer tuning parameters, making it particularly suitable for real-world engineering applications.