摘要
为了提高无线传感器网络定位算法的精确度,利用改进的沙猫群算法优化基于信号强度指示的定位(LISCSO)算法.首先,利用Tent混沌映射优化初始种群,丰富种群多样性;其次,令平衡参数非线性化并引入麻雀警戒机制,提高算法寻优能力;然后,结合高斯和柯西变异的优势,避免陷入局部最优;最后,构建目标函数,利用改进的沙猫群优化算法(ISCSO)进行寻优,计算定位结果.仿真结果表明:ISCSO的初始种群分布更为均匀,为后续寻优建立了基础优势;在多种类型测试函数下具有良好的寻优效果;保持环境参数相同,在不同锚节点数下,LISCSO算法相较于对比算法定位误差降低了16.76%,25.91%,31.36%和12.76%,在不同通信半径下,定位误差降低了14.75%,19.52%,28.30%和11.29%,在不同噪声标准差下,定位误差降低了41.18%,25.93%,45.95%和20.11%,证明LISCSO算法具有更准确的定位结果.
In order to improve the accuracy of the positioning algorithm of wireless sensor networks,the improved sand cat swarm algorithm was used to optimize the positioning algorithm based on signal strength indication(LISCSO).First,the Tent chaotic mapping was used to optimize the initial population and enrich the population diversity.Secondly,the balance parameters were nonlinear and sparrow warning mechanism was introduced to improve the optimization ability of the algorithm.Then,the advantages of Gauss and Cauchy variation were combined to avoid falling into local optimality.Finally,the objective function was constructed,and the improved sand cat swarm optimization(ISCSO) was used to search and calculate the positioning results.The simulation results show that the initial population distribution of ISCSO is more uniform,which establishes a basic advantage for the subsequent optimization.It has good optimization effect under various types of test functions.Under different number of anchor nodes,the positioning error of LISCSO algorithm is reduced by 16.76%,25.91%,31.36% and 12.76% compared with the comparison algorithm.Under different communication radii,the positioning error is reduced by 14.75%,19.52%,28.30% and 11.29%.Under different noise standard deviations,the positioning errors are reduced by 41.18%,25.93%,45.95% and 20.11%.These results show that LISCSO algorithm has more accurate localization results.
作者
余修武
刘胤昊
李登峰
张可
刘永
YU Xiuwu;LIU Yinhao;LI Dengfeng;ZHANG Ke;LIU Yong(School of Resource Environment and Safety Engineering,University of South China,Hengyang 421001,Hunan China;School of Electrical Engineering,University of South China,Hengyang 421001,Hunan China;College of Physicsand Optoelectronic Engineering,Shenzhen University,Shenzhen 518000,Guangdong China)
出处
《华中科技大学学报(自然科学版)》
北大核心
2025年第3期109-116,共8页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
湖南省自然科学基金资助项目(2024JJ5338)
国家自然科学基金资助项目(11875164)。
关键词
无线传感器网络
定位算法
沙猫群优化算法
麻雀警戒机制
信号强度指示
wireless sensor network
location algorithm
sand cat swarm optimization
sparrow warning mechanism
signal strength indication
