摘要
DV-Hop算法在定位过程中,由于信标节点与待定位节点之间的距离估算存在较大的定位误差,使得定位精度不佳。将免疫机制引入粒子群算法中,提出了免疫粒子群优化的DV-Hop算法。利用免疫粒子群算法优化待测节点的位置坐标,当PSO算法陷入局部最优解时,通过免疫抗体的选择、促进和抑制机制产生新的粒子空间,使该算法跳出局部最优值,收敛于全局最优解。MATLAB仿真实验表明,在相同实验环境下与经典的DV-Hop算法和常规的粒子群改进的DV-Hop算法比较,所提算法有效地降低了定位误差。
Larger location errors could occur in the results given by the Distance Vector-Hop（ DV-Hop） algorithm improved with the Particle Swarm Optimization（ PSO） because of the possible local optimization of the PSO. In this paper,the immune mechanism is introduced into PSO,and the immune particle swarm optimization DV-Hop algorithm is proposed. The immune particle swarm algorithm is used to optimize the position to replace the maximum likelihood estimation method to calculate the position coordinates. When the PSO algorithm falls into the local optimal solution,the new particle space is generated by the selection,promotion and inhibition mechanism of the immune antibody,the optimal value converges to the global optimal solution. MATLAB simulation results show that compared with the classical DV-Hop algorithm and the conventional particle swarm optimization DV-Hop algorithm in the same experimental environment,the improved algorithm of this paper effectively reduces the positioning error.
作者
吴珍珍
方旺盛
Wu Zhcnzhcn;Fang Wangshcng(Information Engineering Institute, Jiangxi University of Science and Technology, Ganzhou 341000, China)
出处
《信息技术与网络安全》
2018年第4期84-87,共4页
Information Technology and Network Security
