Abstract

The vertex cover (VC) problem has been studied for past few years, and some centralized and distributed algorithms have been proposed. In this paper, we propose the so-called random memory length adaptive (RMLA) algorithm for the VC problem in networks. Any initial state can reach the stable pure strict Nash equilibrium state through finite iterations by the RMLA algorithm. We find that by setting the minimum edge-keeping probability reasonably, the convergence rapidity and accuracy can be improved simultaneously. Our algorithm also removes the requirement for consistent node memory length. Through theoretical analysis and intensive numerical simulations, we verify the convergence and effectiveness of the RMLA algorithm.

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