Swarm intelligence optimization algorithms have demonstrated excellent performance in solving complex optimization problems. Their core idea is to simulate the collective behavior of organisms in nature to achieve search and learning. This paper proposes a novel swarm intelligence optimization algorithm based on the foraging, migration, predator avoidance, and social behavior of snapper (SOA). This algorithm achieves dynamic search for individuals through four types of behaviors: local foraging, global migration, defense and avoidance, and social learning. It also introduces an energy adaptation mechanism, a social weight mechanism, and a historical trajectory reference strategy to improve the algorithm's global search capability and local exploration accuracy. This paper provides a detailed modeling and mathematical formulation of the algorithm, analyzing its convergence, global exploration capability, local refinement, and swarm intelligence characteristics. This algorithm is applicable to continuous optimization, multimodal functions, combinatorial optimization, and multi-objective optimization problems, and has broad application prospects.