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混合粘液模具和算术优化算法与随机中心学习和重启突变.

Hongmin Chen1, Zhuo Wang1, Heming Jia1

  • 1Department of Information Engineering, Sanming University, Sanming 365004, China.

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概括
此摘要是机器生成的。

本研究介绍了一种混合优化算法,结合了粘液模具算法 (SMA) 和算术优化算法 (AOA),提高了复杂函数的性能. 这种新的方法提高了对汇率的准确性和全球搜索效率,以实现元启发式优化.

关键词:
算术优化算法算法的算术优化算法突变战略的突变策略随机中心解决方案策略策略.重新启动战略重新启动战略粘模具算法 粘模具算法 粘模具算法

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 优化算法 优化算法

背景情况:

  • 模算法 (SMA) 在全球搜索中表现出色,但在后期的振荡中扎.
  • 算术优化算法 (AOA) 通过乘法/除法运算符提供强大的随机性和收性.
  • 现有的算法在复杂的功能优化和找到最佳位置方面面临着挑战.

研究的目的:

  • 开发一个混合优化算法,整合SMA和AOA.
  • 提高全球搜索效率,人口多样性和趋同准确性.
  • 在复杂的优化任务中解决单个算法的局限性.

主要方法:

  • 整合SMA和AOA,取代SMA与AOA运营商的融合阶段.
  • 整合一个随机的中央解决方案战略,以改善全球搜索和多样性.
  • 实施重新启动和突变策略,以提高趋同准确度和后期优化.

主要成果:

  • 混合算法 (RCLSMAOA) 在对各种测试函数的比较实验中显示出有效性.
  • 统计测试 (威尔科克森等级和,弗里德曼) 证实了业绩的提高.
  • 与单个方法相比,增强的算法显示出更高的收精度和优化能力.

结论:

  • 建议的混合粘液模具和算术优化算法 (RCLSMAOA) 对复杂的优化问题是有效的.
  • 随机中央学习,突变和重启策略的整合显著提高了性能.
  • 该算法显示了在实际工程问题中应用的前景.