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多策略改进MPA在无线传感器网络中的应用

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针对无线传感器网络中非测距节点定位算法自身存在算法定位误差较大的缺陷,提出了一种MMPA-3DDV-Hop算法.该算法先利用多通信半径细化节点间的跳数,然后添加了修正因子修正平均跳距,接着采用多策略融合改进MPA算法计算待定位节点位置最优解.多策略改进MPA算法首先利用Singer混沌映射策略对种群进行初始化,克服种群初始化的盲目性.其次,采用t-分布扰动策略来提升算法的全局搜索能力,在增加搜索空间多样性的前提下,达到快速收敛;最后,引入变异策略和小概率策略,将二者相结合来避免该算法陷入局部最优,对改进算法在复杂度、收敛性和稳定性方面进行性能测试,测试结果表明改进后的MPA算法具有较好的收敛性和稳定性.仿真结果表明:相比3DDV-Hop算法、多通信半径算法以及3D-VNDV-Hop算法,MMPA-3DDV-Hop算法的归一化定位误差平均降低了 21.3％、13％与5.7％左右,尽管算法的平均运行时间略有增加,但有效提高了算法的定位精度.
Application of Multi-policy Improved MPA in Wireless Sensor Networks
Aiming at the defect of large algorithm positioning error in non-ranging node positioning algorithm in wireless sen-sor network,a MMPA-3DDV-Hop algorithm was proposed.Firstly,the algorithm used multiple communication radii to refine the number of hops between nodes,then correction factors were added to correct the average hop distance,and then multi-policy fu-sion was used to improve the MPA algorithm to calculate the optimal solution of the position of the node to be located.The multi-policy improvement MPA algorithm was first introduced,and the Singer chaos mapping strategy was first used to initialize the pop-ulation to overcome the blindness of population initialization.Secondly,the t-distribution disturbance policy was adopted to im-prove the global search capability of the algorithm,and achieve fast convergence under the premise of increasing the diversity of the search space.Finally,the mutation policy and the small probability policy were introduced to avoid the algorithm falling into local optimum,and the performance test of the improved algorithm in terms of complexity,convergence and stability was carried out,and the test results show that the improved MPA algorithm has good convergence and stability.The simulation results show that compared with the 3DDV-Hop algorithm,multi-communication radius algorithm and 3D-VNDV-Hop algorithm,the normal-ized positioning error of MMPA-3DDV-Hop algorithm is reduced by about 21.3％,13％and 5.7％on average,although the av-erage running time of the algorithm increases slightly,but the positioning accuracy of the algorithm is effectively improved.

3D DV-Hopcorrection factorSinger mappingt-distributed perturbation strategycross-mutation strategy

彭铎、张倩、陈江旭、吴海涛

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兰州理工大学计算机与通信学院

三维DV-Hop 修正因子 Singer映射 t-分布扰动策略 交叉变异策略

国家自然科学基金项目国家自然科学基金项目甘肃省科技计划甘肃省创新基金甘肃省教育厅:研究生"创新之星"项目

622650106206102423YFGA00622022A-2152023CXZX-481

2024

仪表技术与传感器
沈阳仪表科学研究院

仪表技术与传感器

CSTPCD北大核心
影响因子：0.585
ISSN：1002-1841
年,卷(期)：2024.(5)