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相关概念视频

Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Updated: Jul 16, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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增强的双选择鱼群策略,以优化无线传感器网络的网络寿命和稳定性.

Allam Balaram1, Rajendiran Babu2, Miroslav Mahdal3

  • 1Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad 500043, India.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
概括

本研究介绍了用于无线传感器网络 (WSN) 的增强双选择群优化. 这种新方法显著提高了网络寿命和稳定的能量,同时减少了延迟,以实现高效的WSN运行.

关键词:
双重机制是双重的机制.剥削 剥削 剥削 使用勘探 勘探 勘探 是一个克里尔鱼群的群体延迟时间 延迟时间稳定的稳定性 稳定的稳定性

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 优化算法 优化算法

背景情况:

  • 无线传感器网络 (WSNs) 面临着重要的能源管理挑战.
  • 网络寿命,覆盖范围和数据聚合的最大化是关键的运营目标.
  • 高效的节能对于传感器节点部署和可扩展性至关重要.

研究的目的:

  • 引入一个增强的双选择鱼群 (KH) 优化集群计划,以资源高效的WSNs.
  • 通过优化节点部署和集群,解决WSN中的节能挑战.
  • 提高整体能源利用率,减少节点间的通信.

主要方法:

  • 开发了一个增强的双选择鱼群 (KH) 优化聚类方案.
  • 实施了动态分层机制,以防止重复的集群头选择.
  • 利用修改后的基于鱼的聚类方法来加强开发和勘探.

主要成果:

  • 在网络寿命中实现了23.21%的增强.
  • 稳定能量增加了19.84%.
  • 与基准方法相比,网络延迟减少了22.88%.

结论:

  • 拟议的KH优化集群方案为WSN能源管理提供了更有效和可靠的解决方案.
  • 动态分层和双选择机制有助于提高网络性能.
  • 这种方法有效地解决了WSN运营效率和寿命的关键挑战.