Snow Goose Optimization Algorithm

Swarm optimization algorithms, by simulating the cooperative behavior of biological groups in nature, exhibit strong robustness and adaptability in complex optimization problems. However, most existing optimization algorithms based on animal behavior still rely on static or semi-static leadership structures, single-point optimal guidance strategies, and weak temporal memory mechanisms, making them prone to premature convergence and search stagnation in high-dimensional or multimodal problems. To address this, this paper proposes a novel swarm optimization method—the Snow Goose Optimization Algorithm (SGOA). This algorithm uses the formation coordination, leader rotation, airflow perception, and path correction behaviors of snow geese during long-distance migration as biological prototypes. By introducing mechanisms such as airflow shadow prediction, asymmetric formation self-evolution, leader fatigue resignation, migration turnaround search, and fuzzy endpoint attraction, it achieves joint control over search direction, search scale, and search stability. The proposed algorithm significantly enhances the dynamism and diversity of the search process without relying on complex parameters, providing a new approach to solving complex continuous optimization problems.