基于粒子群算法的WSN覆盖优化

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-28 Cooperative journals: 《计算机应用研究》

郭超 杨宇轩 胡荣磊 肖超恩 王建新 徐凤麟

Abstract： At present, particle swarm optimization coverage algorithm has been widely applied due to its advantages such as fewer parameters; faster computation speed and easier algorithm. However, there are still some defects, for example the slow convergence speed and falling into the local optimal value easily, causes "premature". In order to improve the performance of wireless sensor network, the distribution of nodes and coverage scheme was studied. A Quasi-physical particle swarm optimization algorithm based on inertia weight was proposed. The quasi-gravitational force and quasi-coulomb force in the Quasi-physical force algorithm were combined with the particle swarm optimization algorithm. It enhances the global search ability, converges to the global optimal solution faster, and reduces the consumption and repeated covering. The simulation results prove that the faster global convergences, higher coverage, lower rate of repeat coverage are achieved than the basic particle swarm optimization and standard particle swarm optimization particle swarm optimization hm based on inertia weight.

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