Wolf Pack Algorithm Optimization with MATLAB Implementation

The Wolf Pack Algorithm is an optimization technique inspired by the natural hunting behavior of wolf packs. This algorithm treats the optimal solution of a problem as the "prey" within the wolf pack system, mimicking the wolves' cooperative hunting strategies to search for optimal solutions. In MATLAB implementation, we can enhance the algorithm's performance through intelligent control mechanisms, enabling faster convergence to global optima. Key implementation aspects include simulating wolf hierarchy (alpha, beta, delta wolves), designing movement patterns for encircling prey, and implementing communication mechanisms for information sharing. The MATLAB code typically involves population initialization, fitness evaluation functions, position updating rules based on wolf roles, and convergence criteria checking. This algorithm finds extensive applications across modern technological domains, including model optimization in machine learning, portfolio optimization in financial risk management, engineering design optimization, and resource allocation problems. By studying and refining the Wolf Pack Algorithm, researchers can not only deepen their understanding of optimization methodologies but also develop more efficient solutions for practical engineering and scientific challenges. The MATLAB implementation often utilizes matrix operations for efficient population management, incorporates adaptive step-size control for balance between exploration and exploitation, and employs visualization tools to monitor the optimization process in real-time.