When addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regions. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm based on a dual-population cooperative correlation (CMOEA-DCC). Under the CMOEA-DDC framework, the system maintains two independently evolving populations: the driving population and the conventional population. These two populations share information through a collaborative interaction mechanism, where the driving population focuses on objective optimization, while the conventional population balances both objectives and constraints. To further enhance the performance of the algorithm, a shift-based density estimation (SDE) method is introduced to maintain the diversity of solutions in the driving population, while a multi-criteria evaluation metric is adopted to improve the feasibility quality of solutions in the normal population. CMOEA-DDC was compared with seven representative constrained multi-objective evolutionary algorithms (CMOEAs) across various test problems and real-world application scenarios. Through an in-depth analysis of a series of experimental results, it can be concluded that CMOEA-DDC significantly outperforms the other competing algorithms in terms of performance.

Background: In elderly community services, information technology is reshaping the daily lives of older adults in unprecedented ways. It effectively addresses the issue of frailty in the community by strengthening support networks and dynamic risk management. Despite its vast potential, there remains a need to explore further enabling methods in the realm of elderly community services. Objectives: This study aims to provide a significant theoretical and practical foundation for information technology in this field by systematically analyzing the progress and trends of digital transformation facilitated by information technology. Materials and method: To map the advancements and emerging trends in this evolving field, this study conducts a bibliometric analysis of 461 relevant publications from the Web of Science Core Collection (2004–2024). The research employs bibliometric methods and utilizes tools such as CiteSpace and VOSViewer to analyze collaborations, keywords, and citations, as well as to perform data visualization. Results: The findings indicate that current research hotspots mainly focus on “community care”, “access to care”, “technology”, and “older adults”.Potential development trends include (1) further exploration of information technology in elderly care to provide more precise health management solutions; (2) systematically building community elderly service systems to offer more detailed elderly care services; (3) strengthening interdisciplinary information sharing and research collaboration to drive innovation in community elderly care models; and (4) introducing targeted policy and financial support to improve the specific implementation framework of information technology in elderly community services. Conclusions: This study provides empirical support for the development of relevant theories and practices. Furthermore, the research outcomes offer valuable insights into business opportunities for practitioners and provide important recommendations for formulating elderly service policies.