基于自适应非线性因子杂草算法的WSN覆盖优化  

WSN Coverage Optimization by Adaptive Nonlinear Factor based Weed Algorithm

作　　者：付波[1] 黄晓啸 赵熙临[1] 权轶[1] 贺章擎[1] FU Bo;HUANG Xiaoxiao;ZHAO Xilin;QUANG Yi;HE Zhangqing(School of Electrical and Electronic Engineering,Hubei Univ.of Tech.,Wuhan 430068,China)

机构地区：[1]湖北工业大学电气与电子工程学院,湖北武汉430068

出　　处：《湖北工业大学学报》2023年第2期7-10,26,共5页Journal of Hubei University of Technology

基　　金：湖北省自然科学基金项目(2020CFB814)。

摘　　要：无线传感器网络(WSN)的覆盖率与区域内的传感器节点分布密切关联,而现有传感器分布算法存在收敛速度慢、易陷入局部极值等问题。对此,提出了一种基于自适应非线性因子杂草算法(HA-IWO)的传感器节点分布优化方法。首先,在初始阶段,利用Halton序列产生偏差很小的初始点,使种群分布更均匀;其次,在种群扩散阶段,将非线性调和因子设置为根据迭代次数自适应产生,以调整搜索步长,解决算法易陷入局部最优的问题。最后,通过4组标准函数测试与WSN覆盖优化仿真对该算法进行验证。仿真实验表明:相比于标准杂草算法,改进后的算法具有收敛速度快、覆盖率高的优点,能有效解决WSN覆盖优化问题。The coverage rate of wireless sensor network(WSN)is closely related to the distribution of sensor nodes in the area,and existing sensor distribution algorithms have problems such as slow convergence speed and easy to fall into local extreme values.In this regard,this paper proposes an optimization method for sensor node distribution based on adaptive nonlinear factor weed algorithm(HA IWO).First,in the initial stage,the Halton sequence is used to generate initial points with small deviations to make the population distribution more uniform;second,in the population diffusion stage,the nonlinear harmonic factor is set to be adaptively generated according to the number of iterations to adjust the search step size.Solve the problem that the algorithm is easy to fall into the local optimum.Finally,the algorithm is verified by 4 sets of standard function tests and WSN coverage optimization simulation.Simulation experiments show that the algorithm has the advantages of fast convergence speed and high coverage rate,and can effectively solve the WSN coverage optimization problem.

关 键 词：WSN覆盖率 杂草算法 节点分布 Halton序列 非线性调和因子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP212.9[自动化与计算机技术—控制科学与工程]