融合柯西折射反向学习和变螺旋策略的WSN象群定位算法
作者:
作者单位:

南华大学

作者简介:

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中图分类号:

TP273

基金项目:

湖南省重点研发计划项目(2018SK2055),国家自然科学基金项目(11875164)


Cauchy Refraction opposition-based learning and Variable Helix mechanism of Elephant herding localization algorithm in WSN
Author:
Affiliation:

University of South China

Fund Project:

Key Research and Development Projects of Hunan Province(2018SK2055), National Natural Science Foundation of China(11875164)

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    摘要:

    针对现有无线传感器网络(WSN)优化算法在定位过程中收敛速率慢和误差大的问题,该文提出一种基于柯西折射反向学习和变螺旋机制的象群节点定位算法。首先,利用具有遍历性和随机性的Logistic混沌映射初始化种群,丰富种群多样性,加快算法收敛速率。其次,将折射反向学习机制与柯西变异相融合以随机扰动族长位置,避免算法陷入局部最优。最后,在氏族分离过程中引入自适应变螺旋策略更新病态大象位置,提升算法全局搜索能力。仿真结果表明,与现有WSN优化算法相比,该文提出的改进象群优化算法在定位精度和收敛速率方面得到明显提升。

    Abstract:

    Aiming at the problem of slow convergence and large errors in the positioning process of the existing wireless sensor network (WSN) optimization algorithm, this paper proposes a Cauchy refraction opposition-based learning and variable helix mechanism of elephant herding localization algorithm. Firstly, the population is initialized by using Logistic chaotic map with ergodicity and randomness to enrich the population diversity and accelerate the algorithm convergence rate. Secondly, the refraction opposition-based learning mechanism is combined with Cauchy mutation to randomly disturb the position of the patriarch to prevent the algorithm from falling into the local optimum. Finally, an adaptive variable helix strategy is introduced to update the position of ill elephants in the process of clan separation, which improves the global search ability. The simulation results show that the improved elephant herding optimization algorithm proposed in this paper has significantly improved positioning accuracy and convergence rate compared with the existing WSN optimization algorithm.

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历史
  • 收稿日期:2021-02-24
  • 最后修改日期:2021-07-21
  • 录用日期:2021-07-29
  • 在线发布日期: 2021-09-01
  • 出版日期: