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多策略融合改进了海优化算法,用于无线传感器网络中的覆盖率优化.

Ling Li1, Youyi Ding2, Xiancun Zhou1

  • 1School of Electronic Information and Artificial Intelligence, West Anhui University, Lu'an 237012, China.

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概括
此摘要是机器生成的。

一个改进的Walrus Optimization (WO) 算法 (IMWO) 通过整合差异进化,LSC映射和基于Beta对立的学习来增强全球探索和稳定性. IMWO在基准测试和无线传感器网络覆盖率优化方面取得了卓越的性能.

关键词:
后勤 相对应 相对应 地图基于对立的Beta学习不同的进化是不同的进化.摩鱼优化优化 摩鱼优化无线传感器网络是无线传感器网络.

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科学领域:

  • 计算智能是一种计算智能.
  • 超启发式优化优化
  • 工程优化工程优化

背景情况:

  • 摩优化 (WO) 算法提供快速融合,但与局部最佳和不稳定性作斗争.
  • 高维和复杂的工程问题需要强大的优化技术.

研究的目的:

  • 开发一个改进的海优化 (IMWO) 算法,解决原始WO的局限性.
  • 为了增强全球勘探,搜索稳定性和WO算法的收精度.

主要方法:

  • 整合差异进化/最佳/1 (DE/最佳/1) 突变,以改善探索.
  • 应用物流-正弦-正弦 (LSC) 映射来增强搜索动态.
  • 纳入基于对立的Beta学习 (Beta-OBL) 策略,以实现更好的初始化和融合.

主要成果:

  • 在CEC2017和CEC2022基准套件上,IMWO算法获得了优异的平均健身排名 (1.66和1.33).
  • 在基准评估中,IMWO在基准评估中表现优于原始的WO和其他六种最先进的元启发术.
  • 在无线传感器网络 (WSN) 覆盖率优化方面,IMWO实现了高平均覆盖率 (95.86%和96.48%).

结论:

  • 拟议的IMWO算法显示了全球勘探,稳定性和收精度的显著改进.
  • 在解决复杂的现实世界工程优化问题方面,IMWO证明是有效和强大的,例如WSN覆盖范围.
  • DE/best/1,LSC映射和Beta-OBL的协同集成提高了metaheuristic的性能.