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Open Access

ARTICLE

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

Atef Gharbi1, Mohamed Ayari2, Nasser Albalawi3, Ahmad Alshammari3, Nadhir Ben Halima4,*, Zeineb Klai3
1 Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
2 Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
3 Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
4 Department of Information Technology, Community College of Qatar, Doha, Qatar
* Corresponding Author: Nadhir Ben Halima. Email: email
(This article belongs to the Special Issue: Control Theory and Application of Multi-Agent Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.075474

Received 02 November 2025; Accepted 08 January 2026; Published online 28 January 2026

Abstract

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.

Keywords

Ant colony optimization; multi-agent systems; deep neural networks; meta-adaptive learning; Lévy flight; differential privacy; swarm intelligence

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