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相关概念视频

Predator-Prey Interactions02:39

Predator-Prey Interactions

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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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相关实验视频

Updated: Jun 3, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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无线传感器网络覆盖率优化使用修改后的海洋捕食者算法

Guohao Wang1, Xun Li1

  • 1School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

一个修改后的海洋捕食者算法 (MMPA) 增强了用于森林火灾监测的无线传感器网络节点部署. 与其他算法相比,MMPA实现了更高的覆盖率和节点分布统一性.

关键词:
优化覆盖范围的优化海洋捕食者算法 海洋捕食者算法无线传感器网络是一个无线传感器网络.

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科学领域:

  • 计算机科学 计算机科学
  • 优化算法 优化算法
  • 无线传感器网络 无线传感器网络

背景情况:

  • 在森林火灾监测中随机部署无线传感器网络节点会导致覆盖问题.
  • 现有的元启发算法可能无法为有效监控提供最佳节点分布.

研究的目的:

  • 为解决森林火灾监测无线传感器网络的覆盖率问题.
  • 为改进节点部署和覆盖提供修改后的海洋捕食者算法 (MMPA) 提出和评估.

主要方法:

  • 一个修改后的海洋捕食者算法 (MMPA),包括帐映射,混合搜索,金正弦机制和阶段调整策略.
  • 使用CEC2017和基准测试功能进行性能评估.
  • 在三个场景中进行覆盖测试,将MMPA与其他元启发算法进行比较.

主要成果:

  • 与MPA,灰狼优化器,正弦共弦算法和海马优化器相比,MMPA表现出优越的优化能力和稳定性.
  • MMPA实现了更高的平均覆盖率:91.8% (情景1),95.98% (情景2) 和93.88% (情景3).
  • MMPA导致无线传感器网络节点的分布更加均.

结论:

  • 拟议的MMPA有效地解决了用于森林火灾监测的无线传感器网络的覆盖率问题.
  • MMPA在覆盖率和节点分布统一性方面提供了显著的改进.
  • 对于这个应用程序,MMPA显示出优于现有的基于元启发的算法.