遗传粒子群优化的DV-Hop定位算法

发布时间：2018-02-16 05:50

本文关键词： 无线传感器网络节点定位遗传粒子群算法平均每跳距离前摄估计　出处：《传感技术学报》2017年07期 　论文类型：期刊论文

【摘要】：用常规粒子群改进的DV-Hop算法由于粒子易陷入局部最优而导致较大的定位误差,对此,提出了结合遗传粒子群的DV-Hop定位(GAPSO-DV-Hop)算法。首先根据最大理想跳数筛选锚节点,计算加权平均每跳距离,权重采用锚节点之间距离、最小跳数、以及通信半径构成;其次,用遗传机制改进粒子群算法以代替最小二乘法,所作改进包括使用前摄估计缩小粒子搜索范围、根据遗传算法中的交叉策略生成待交叉粒子队列,并在每次迭代后选取最差个体做动态变异。仿真结果表明,在相同环境下,所提GAPSO-DV-Hop算法的定位精度明显高于常规DV-Hop算法以及其他对比算法。
[Abstract]:The improved DV-Hop algorithm based on conventional particle swarm optimization (PSO) leads to large localization errors due to the fact that particles are prone to fall into local optimum. In this paper, a GAPSO-DV-Hopp algorithm combined with genetic particle swarm optimization (PSO) is proposed. Firstly, anchor nodes are selected according to the maximum ideal number of hops. The weighted average distance per hop is calculated, the weight is composed of the distance between anchor nodes, the minimum number of hops, and the communication radius. Secondly, the particle swarm optimization algorithm is improved by genetic mechanism to replace the least square method. The improvements include reducing the range of particle search by proactive estimation, generating queue of particles to be crossed according to the crossover strategy in genetic algorithm, and selecting the worst individual for dynamic mutation after each iteration. The simulation results show that, under the same environment, The location accuracy of the proposed GAPSO-DV-Hop algorithm is obviously higher than that of the conventional DV-Hop algorithm and other comparison algorithms.
【作者单位】：江南大学轻工过程先进控制教育部重点实验室;
【基金】：国家自然科学基金项目(61373126) 江苏省自然科学基金项目(BK20131107)
【分类号】：TN929.5;TP18;TP212.9

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