WSN中节点分布的协方差矩阵自适应优化策略

Covariance Matrix Adaptation Optimization Based Node Distribution in Wireless Sensor Networks

针对无线传感器网络(WSNs)中传感器节点的分布优化问题,提出了一种基于协方差矩阵自适应进化策略(CMA-ES)的网络节点分布优化方法。首先,以最大化网络的区域覆盖率为目标建立问题的求解模型,然后,采用CMA-ES算法对模型求解得到网络最优的节点位置分布方案。仿真对比实验表明:CMA-ES算法可以很好地解决无线传感器网络节点的分布优化问题,相比于传统遗传算法、基本粒子群算法和差分进化算法,表现出较快的寻优速度和更高的区域覆盖率。

Aiming at the node distribution optimization problem of Wireless Sensor Networks（ WSNs）,then a method for network node distribution optimization based on Covariance Matrix Adaptation Evolution Strategy（ CMA- ES） was proposed. Firstly,for the goal of maximizing network area coverage rate,the solving model was established,and then the model was solved using CMA- ES algorithm,and the optimal node distribution scheme for WSNs was got. Simulation comparative experiments show that CMA- ES algorithm can efficiently solve the distribution optimization problem of wireless sensor network node,comparing with traditional genetic algorithm,particle swarm optimization algorithm and differential evolution algorithm,and CMA- ES achieves faster optimization speed and higher area coverage rate.