A Distributed Dual-Network Meta-Adaptive Framework for Scalable and Privacy-Aware Multi-Agent Coordination

This paper presents Dual Adaptive Neural Topology (Dual ANT), a distributed dual-network meta-adaptive framework that enhances ant-colony-based multi-agent coordination with online introspection, adaptive parameter control, and privacy-preserving interactions. This approach improves standard Ant Colony Optimization (ACO) with two lightweight neural components: a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations. To preserve the privacy of individual trajectories in shared pheromone maps, we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal. The resulting system enables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions, including varying obstacle layouts, uncertain target locations, and time-varying disturbances. Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence, higher robustness, and improved scalability compared to advanced baselines such as Multi-Strategy ACO and Hierarchical ACO. The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Lévy flight exploration only when necessary.