The low coverage rate of wireless sensor network is due to the high concentration of sensor nodes in random deployment,an improved grey wolf optimization algorithm was proposed and its application to wireless sensor network nodes deployment. Firstly, chaos mapping is used to initialize the algorithm population to improve population diversity;Secondly,the convergence factors of grey wolf optimization algorithm are improved to balance global and local search capability and the optimization of the algorithm is improved;Finally,by fusion mutationδwolf to solve the problem of local extremum. The simulation test show that the improved grey wolf optimization algorithm the WSN coverage rate and optimization velocity has been improved,compared with the standard grey wolf optimization algorithm.%根据无线传感网络节点在随机部署时存在聚集程度高导致覆盖率低的问题,提出了一种改进的灰狼优化算法,并将其应用于无线传感网络节点的优化覆盖.首先,利用混沌算法进行算法种群的初始化,以提高种群多样性;其次,在灰狼算法的基础上改进其收敛因子,平衡全局和局部搜索能力,提高算法中后期的优化能力;最后,对δ狼进行融合变异以改善局部极值问题.仿真实验表明,将改进后的灰狼优化算法应用于WSN节点部署优化中,与标准灰狼优化算法相比加快了优化速度,网络覆盖率提高了3％.
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