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A Dual-Layer Fuzzy Consensus Optimization Algorithm for Exponential Convergence in Distributed Networks
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DOI：10.1109/TFUZZ.2025.3614729.png)
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Abstract
En 中文
Distributed epidemic network scenarios have been widely employed as the foundational basis for robust and adaptive consensus estimation, leveraging data-driven methods to manage uncertainties in both epidemic parameters and network conditions. Traditional fixed-step gossip consensus protocols exhibit suboptimal performance due to their inability to dynamically adapt to fluctuations in local uncertainty and network quality. In this article, we propose the dual-layer fuzzy consensus optimization algorithm (DLFCOA) scheme to address these challenges and achieve exponential convergence of the consensus error. Our approach integrates dual-layer Type’2 fuzzy inference with an adaptive step-size mechanism and rigorous Lyapunov stability analysis to dynamically adjust the gossip update rate based on real-time feedback. Experimental results demonstrate that DLFCOA reduces the global consensus error to the order of $10^{-4}$, achieves a convergence rate of approximately 0.12, and requires significantly fewer iterations—around 150 compared to up to 800 in conventional methods—thus offering enhanced scalability, robustness, and efficiency.
Keywords:
Adaptive gossip consensus optimization
distributed epidemic network scenario
dual-layer fuzzy inference mechanism
exponential convergence stability analysis
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