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Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
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一个改进的灰狼算法及其在复杂的室内环境中的本地化研究.

Bing Li1,2,3, Yanxin Hao1,2, Yiyang Cui1,2

  • 1College of Combustion Engineering, Hebei Normal University, Shijiazhuang, 050024, China.

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概括
此摘要是机器生成的。

本研究介绍了一种改进的灰狼优化 (GWO) 算法,用于超宽带 (UWB) 室内定位,显著提高了复杂环境中的准确性. 增强的TIGWO算法克服了传统方法和群体智能的局限性,实现了卓越的定位精度.

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3D室内定位 3D室内定位灰狼优化算法 灰狼优化算法定位精度 定位精度 定位精度集群算法集群算法集群算法集群算法集群算法超宽带技术的超宽带技术

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科学领域:

  • 机器人和自动化机器人与自动化
  • 无线通信系统无线通信系统
  • 优化算法 优化算法

背景情况:

  • 复杂的室内环境对传统的本地化方法构成重大挑战,原因是非视线 (NLOS) 和多路径传播问题.
  • 现有的群集智能优化算法,虽然避免了矩阵反转,但遭受了缓慢的融合和局部最佳,限制了它们在本地化中的实际应用.
  • 超宽带 (UWB) 技术提供了高精度室内定位的潜力,但需要强大的算法来减轻环境干扰.

研究的目的:

  • 提出一种新的和增强的灰狼优化 (GWO) 算法,称为TIGWO,用于提高超宽带 (UWB) 室内定位的准确性.
  • 在复杂的室内环境中解决传统本地化技术和现有的群集智能算法的局限性.
  • 通过与其他已建立的本地化方法进行实验性比较来验证TIGWO算法的有效性.

主要方法:

  • 通过整合四个关键策略开发了TIGWO算法:使用Chan算法进行初始定位的小面积优化,改进的帐地图,非线性收因子,以及具有超限反射机制的健身权重位置更新.
  • 将本地化问题转化为可以通过TIGWO算法解决的函数优化任务,消除了对矩阵反转的需求.
  • 应用了TIGWO算法来确定UWB室内定位系统中的最佳标签位置.

主要成果:

  • 与Chan,Chan-Taylor,粒子群优化 (PSO),鱼优化算法 (WOA) 和标准GWO相比,提议的TIGWO算法在室内定位准确度方面取得了显著的改进.
  • 与Chan相比,实现了59.65%的平均本地化准确度提升,与Chan-Taylor相比,63.41%,与PSO相比40.97%,与WOA相比45.97%,与GWO相比35.44%.
  • 在设备仓库场景中,TIGWO算法产生了较低的定位误差:X轴上为0.129m,Y轴上为0.101m,Z轴上为0.154m.

结论:

  • TIGWO算法通过优化GWO算法以有针对性的策略,有效地提高了复杂环境中的室内定位准确性.
  • 该方法成功克服了传统本地化技术和标准群集智能算法的局限性,提供了更强大的解决方案.
  • 实验结果证实了TIGWO算法在室内UWB定位方面的优势,显示了实质性的准确度增长和较低的误差率.