A Distributed Swarm-Intelligence Framework for Multi-Agent Optimization in Large-Scale Networked Cyber-Physical Systems Under Communication Constraints

Abstract

Networked cyber-physical systems have grown in scale and heterogeneity as sensing, actuation, and computation become pervasive across infrastructure, robotics, and industrial automation. Coordination in these systems increasingly relies on multi-agent optimization under stringent communication constraints, where link capacities fluctuate, quantization is required, and transmissions are intermittent or delayed. Swarm-intelligence approaches provide attractive heuristics for exploration, resilience to nonconvexity, and adaptability to incomplete information, yet their integration with rigorous distributed optimization remains challenging when channels are unreliable and bandwidth is scarce. This paper investigates a distributed swarm-intelligence framework for large-scale multi-agent optimization in settings where only sparse, quantized, and event-triggered communication is feasible. The framework blends consensus-driven gradient tracking with exploration and memory mechanisms reminiscent of swarm dynamics while enforcing communication-awareness through rate-budgeted message scheduling and bias-controlled quantization. Analytical results develop nonasymptotic stationarity guarantees and stability bounds under time-varying graphs, random packet losses, and bounded delays. Complexity estimates relate compute, communication, and memory costs to network mixing, objective smoothness, and noise levels. Empirical studies examine robotic formation, power dispatch, and traffic assignment, emphasizing scalability with thousands of agents and robustness under severe packet-drop regimes. The presentation focuses on principled design choices that reconcile exploration with contraction, reduce variance without suppressing diversity, and allocate bits where they most improve descent. The overall contribution is a cohesive methodology that retains the flexibility of swarm heuristics while delivering predictable behavior in bandwidth-limited, large-scale, and dynamic environments.