Swarm intelligence optimization algorithms have been widely applied in continuous and discrete optimization problems, but existing methods generally implicitly assume that "individuals are points, the search is point movement, and the optimal solution is an attracting point." This assumption often leads to premature convergence, search space collapse, and insufficient exploration ability in high-dimensional, multimodal, and ill-conditioned problems. This paper proposes a novel swarm intelligence optimization method—South American Python Optimization (SAPO). Its core idea is not to model the search subject as a point, but rather as a feasible region with shape, volume, and swallowing potential. The algorithm achieves continuous compression and structural reorganization of the search space by defining a region-level swallowing potential function, anisotropic compression dynamics, predation partial order relations, and a swallowing-induced topological reconstruction mechanism. Unlike traditional swarm intelligence algorithms, SAPO's optimization objective is no longer to directly approximate the optimal point, but to gradually reduce the coverage of the feasible region that may still contain the optimal solution. Theoretical analysis shows that, under the condition that the objective function satisfies Lipschitz continuity, this method has convergence in the sense of coverage preservation. This algorithm provides a new paradigm for swarm intelligence optimization that differs from "point search".