Dual Acceleration Driven Gray Wolf Optimization Network Coverage Algorithm

Abstract

AbstractEnsuring network coverage in complex battlefield environment is the prerequisite for winning intelligent war. Aiming at the problems of low tactical edge network coverage and slow convergence speed in the strong confrontation combat environment, this paper proposes a gray wolf optimization network coverage algorithm driven by double acceleration. The algorithm uses piece wise linear chaotic map to accelerate the optimization speed in the initialization process, and then improves the convergence factor in gray wolf optimization. The optimization speed in the front section is appropriately reduced to ensure the ability of global search and improve the network coverage, the convergence is accelerated in the later stage, which improves the convergence speed of the algorithm. The simulation results show that the algorithm outperforms several typical baseline methods in terms of network coverage and convergence speed.KeywordsTactical edge networkNetwork coverageDouble accelerationGlobal optimization