Communication Structure Adaptive Control and Collaborative Optimization for Multi-Agent Systems

Multi-agent systems(MAS) rely on adaptive communication architectures to coordinate, scale, and maintain robustness in dynamic settings where fixed topologies fail. By adjusting connections under uncertainty, agents preserve connectivity while reducing resource use, yet combining adaptation with collaborative optimization remains fragmented. Reviewing over fifty key works from 2003 to 2025, we present a unified taxonomy of communication paradigms (static vs. dynamic; directed vs. undirected; hierarchical vs. peer‑to‑peer; sparse vs. dense), adaptive‑control strategies (model‑free protocols, learning‑driven topology updates, fault‑resilient controls), and cooperative optimization methods. Advances in distributed consensus, event-triggered messaging, and quantized communication have dramatically lowered bandwidth requirements without sacrificing performance, and algebraic connectivity is shown critical for convergence rates. Despite progress, challenges persist in scalability, privacy/security in distributed coordination, and trust‑aware human–robot interaction. We propose future directions toward privacy-preserving protocols, enhanced communication security, and interpretable coordination frameworks.