Swarm intelligence optimization algorithms are optimization methods based on the simulation of the collective behavior of organisms in nature, capable of efficiently solving complex optimization problems. This paper proposes a novel swarm intelligence optimization algorithm—the Forest Rattlesnake Optimization Algorithm (FROA). This algorithm simulates the hunting behavior, tail warning mechanism, and energy regulation strategies of rattlesnakes in forest ecosystems. Through multi-layered hunting, multi-scale perception, dynamic energy regulation, and hierarchical group cooperation, it achieves efficient global search and local fine-grained optimization. FROA demonstrates powerful search capabilities and adaptive performance in complex, high-dimensional, and multi-modal optimization problems. This paper systematically elaborates on the algorithm modeling, core ideas, mathematical formula description, algorithm flow, and characteristic analysis of FROA, providing new methods and ideas for the research of swarm intelligence optimization algorithms.