As network scale expands and structural complexity increases, the spread of infectious diseases and information often relies on group interactions, exhibiting significantly higher-order characteristics. These higher-order interactions pose substantial challenges in controlling propagation and optimizing immunization in complex networks. Under the constraint of limited immunization resources, accurately identifying key immune nodes to effectively suppress the spread of diseases has become a core issue in network science. However, existing immunization strategies primarily depend on pairwise relationships in networks, which fail to capture the multi-agent, group-level interactions that are commonly present in real-world systems. Hypergraphs, as a modeling framework that naturally represents higher-order interactions, provide a fresh perspective for addressing group-driven propagation and immunization problems. To tackle the hypergraph immunization problem, this paper proposes a novel community-based adaptive ant colony optimization strategy (HACO: Hypergraph Immunization: A Community-based Adaptive Ant Colony Optimization Approach). The method allocates immunization resources at the community level and uses an adaptive ant colony optimization mechanism to effciently search for and optimize the selection of immune nodes. Through a community-based resource allocation strategy, the size and importance of each community guide the distribution of the immunization budget, ensuring precise and effcient application of resources. Combined with an adaptive search mechanism, HACO fine-tunes the immunization process, balancing exploration and exploitation to achieve more effective optimization. Experimental results based on the hypergraph SIR model show that, across four real-world hypergraph datasets and various infection rates, HACO significantly reduces the infection peak and consistently outperforms six baseline methods. Specifically, compared to the best-performing benchmark, HACO further reduces the average infection peak by 2.41%–5.25%. These results highlight that the HACO method provides an effcient optimization framework for propagation control in higher-order networks. The findings have significant implications for epidemic prevention, disease control, and governance in complex systems, particularly those driven by group interactions and higher-order dynamics. This work not only advances the understanding of immunization theory in complex networks but also provides practical solutions with wide applications in public health and network management.