Adaptive Multi-Objective Resource Allocation for Cooperative Control of Networked Agents in Cyber-Physical Systems

In Cyber-Physical Systems (CPS), particularly within intelligent traffic environments, achieving efficient and adaptive coordination among autonomous agents remains a core challenge. This paper presents a distributed multi-objective control framework that enables real-time resource allocation and decision-making across heterogeneous agents. By incorporating a hierarchical dual-layer encoding strategy and dynamic operator adjustment, the approach enhances both global coordination and local adaptability. The method is evaluated through a right-of-way scheduling scenario among autonomous vehicles at intersections, demonstrating improved traffic flow and responsiveness under dynamic conditions. These results underscore the framework's potential as a scalable and constraint-aware solution for real-time multi-agent control in CPS environments.