多智能体系统分布式优化综述与前瞻

With deeper research into multi-agent systems and increasing practical application demands, distributed optimization has garnered significant attention from both academia and industry in recent years. Enhancing algorithmic efficiency has become a core challenge in this field. This paper presents a survey of distributed optimization algorithms for multi-agent systems. First, the research background, fundamental definitions, and core problem models are introduced. Subsequently, the paper systematically reviews classical distributed optimization algorithms. Furthermore, from the two key dimensions of non-ideal communication networks and non-deterministic cost functions, it explores the core design ideas, bottlenecks, and improvement strategies of current algorithms. Finally, this paper presents application examples and unique value of distributed optimization in unmanned systems, industrial Internet of Things, and smart grids. By summarizing and reviewing existing research findings, this paper aims to offer a valuable reference for researchers in the field and provides a forward-looking perspective on future research directions.