Arctic Hare Optimization Algorithm

Swarm intelligence optimization algorithms solve complex optimization problems by simulating the collective behavior of organisms in nature. Arctic hares, living in the cold polar environment, possess strong survival adaptability and rapid movement capabilities, providing inspiration for designing efficient optimization algorithms. This paper proposes an optimization algorithm based on the behavioral characteristics of Arctic hares, called the Arctic Hare Optimization Algorithm (AHOA). This algorithm achieves a balance between global exploration and local exploitation by simulating the Arctic hare's foraging behavior, group cooperation, energy regulation, and multi-scale jumping strategies. This paper details the algorithm's individual modeling, iterative process, mathematical formula mechanism, and algorithmic characteristics, and discusses its application prospects in continuous optimization, discrete optimization, and engineering optimization. Experimental and application results show that the algorithm has fast convergence, strong resistance to local optima, and good scalability.