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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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基于多策略的磁性目标定位方法,改进了灰狼优化器.

Binjie Lu1,2, Zongji Li1, Xiaobing Zhang3,4

  • 1Naval University of Engineering, Wuhan, 430033, Hubei, China.

Scientific reports
|May 2, 2025
PubMed
概括
此摘要是机器生成的。

一个新的多策略改进的灰狼优化器 (MSIGWO) 提高了磁性目标状态估计的准确性. 这种先进的算法在复杂问题上优于现有的方法,提高了实际应用中的可靠性.

关键词:
适应性维度学习是适应性的.适应性征收飞行 适应性征收飞行动态重量是动态的重量.灰狼优化器 灰狼优化器磁性目标状态估计估计多种群的融合进化.非线性收因子的非线性收因子帐混沌地图绘制帐混沌地图

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算机科学 计算机科学
  • 优化算法 优化算法

背景情况:

  • 磁性目标状态估计至关重要,但受到准确性问题的挑战.
  • 现有的灰狼优化器 (GWO) 算法在复杂的问题上扎.
  • 需要增强的群体智能来提高估计准确度.

研究的目的:

  • 为磁性目标状态估计提出和评估一个多策略改进的灰狼优化器 (MSIGWO).
  • 为了提高磁性目标状态估计的准确性和稳定性.
  • 在复杂的优化场景中解决传统GWO的局限性.

主要方法:

  • 引入了帐混沌映射,以改善初始化和多样性.
  • 实施多种群聚变演化策略,以加强搜索.
  • 利用非线性收因子和动态权重策略来实现平衡的勘探/开采.
  • 整合了自适应的维度学习和自适应的Levy飞行,以实现强大的优化.

主要成果:

  • 在CEC2018基准函数上,MSIGWO表现优于GWO及其变体.
  • 统计指标和弗里德曼测试证实了MSIGWO的提高准确性和全球搜索能力.
  • 该算法被证明是有效和适用于磁性目标状态估计问题.

结论:

  • 拟议的MSIGWO算法显著提高了磁性目标状态估计的准确性.
  • MSIGWO提供了增强的人口多样性,融合速度和全球搜索能力.
  • 这种优化的方法为实际的磁性目标状态估计挑战提供了更可靠的解决方案.