基于爬山粒子群优化的移动传感网络定位算法-计算机工程与应用.PDFVIP

84 2016 ，52（5 ） Computer Engineering and Applications 计算机工程与应用 基于爬山粒子群优化的移动传感网络定位算法 1 1 2 吉小洪 ，魏开平 ，胡文杰 1 1 2 JI Xiaohong , WEI Kaiping , HU Wenjie 1.华中师范大学 计算机学院，武汉 430079 2.咸宁职业技术学院 信息工程学院，湖北 咸宁 437100 1.School of Computer, Center China Normal University, Wuhan 430079, China 2.School of Information Engineering, Xianning Vocational Technical College, Xianning, Hubei 437100, China JI Xiaohong, WEI Kaiping, HU Wenjie. Novel mobile wireless sensor network localization algorithm based on hill climbing particle swarm optimization. Computer Engineering and Applications, 2016, 52 （5 ）：84-88. Abstract ：The Monte Carlo localization algorithm suffers the problem of low sampling efficiency and low localization accuracy. Hence a novel mobile wireless sensor network localization algorithm, HCPSO-MCL, based on hybrid particle swarm optimization combined with hill climbing is proposed. It converts the localization problem to global optimization problem. The HCPSO-MCL algorithm is used to correct the estimated localization of MCL, it can determine the location quickly. The simulation results show that, compared with MCL algorithm, HCPSO-MCL has higher localization accuracy and is faster than PSO-MCL in convergence speed. Key words ：wireless sensor network; Monte Carlo localization; hill climbing particle swarm optimization 摘 要：针对蒙特卡洛定位（Monte Carlo Localization ，MCL ）采样效率不高，定位精度较低的问题，提出一种新的基 于爬山法优化策略的移动无线传感网络定位算法HCPSO-MCL （Hill Climbing Particle Swarm Optimization-MCL ）， 将节点定位问题转化为全局优化问题。HCPSO-MCL 算法采用基于爬山策略的混合粒子群优化算法对MCL 的估计 值进行修正，从而实现节点快速准确定位。实验仿真结果表明，HCPSO-MCL 较之于MCL 算法在定位精度上有很大 改进，而且比PSO-MCL （Particle Swarm Optimization-MCL ）算法有更快的收敛性。 关键词：无线传感网络；蒙特卡洛定位；爬山粒子群算法 文献标志码：A 中图分类号：TP399 doi ：10.3778/j.issn. 1002-8331.1505-0168 1 引言 目