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增强的秘书鸟优化算法用于无线传感器网络部署和工程问题.

Qingwen Meng1, Xi Kuang2, Zihan Yu3

  • 1School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China.

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增强的Secretary Bird优化算法 (ASBOA) 提高了优化问题的解决准确性和融合速度. 它在无线传感器网络部署和工程设计挑战方面表现优于现有的方法.

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科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 超听证学是一种超听证学.

背景情况:

  • 无线传感器网络 (WSN) 部署和工程优化问题需要高效的算法.
  • 现有的优化算法如PSO,GWO,DBO和CPO在准确性和融合速度方面存在局限性.
  • 原来的Secretary Bird优化算法 (SBOA) 为开发改进的优化技术提供了基础.

研究的目的:

  • 开发一个增强的Secretary Bird优化算法 (ASBOA),以提高解决方案的准确性和融合速度.
  • 在复杂的优化任务中解决现有算法的局限性.
  • 评估ASBOA在基准功能和现实问题上的表现.

主要方法:

  • 引入差异性协作搜索机制,以避免局部优化.
  • 实施一个最佳的边界控制机制,以加强融合.
  • 利用信息保留控制机制,有效地更新群体.
  • 使用CEC2017和CEC2022基准测试函数进行评估.
  • 应用于WSN部署和工程设计问题 (三条杆架,张力/压缩弹,悬臂梁).

主要成果:

  • 在23/30 (30D) 和26/30 (100D) CEC2017功能以及9/12 CEC2022功能上,ASBOA表现出卓越的性能.
  • 与其他算法相比,ASBOA表现出更快的融合速度和更强大的稳定性.
  • 在WSN部署中,ASBOA实现了88.32%的覆盖率,比SBOA有1.12%的改进.
  • 在工程设计问题上,ASBOA始终优于竞争对手的方法.

结论:

  • 增强的Secretary Bird优化算法 (ASBOA) 显示出强大的整体性能和复杂的优化问题的巨大潜力.
  • 与SBOA和其他领先的算法相比,ASBOA提供了更好的准确性和更快的融合.
  • 对于高维多式和受约束优化问题,需要进一步精细化,以提高稳定性和参数灵敏度.