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  1. 首页
  2. 快速随机基于对立的学习 Aquila优化算法
  1. 首页
  2. 快速随机基于对立的学习 Aquila优化算法

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快速随机基于对立的学习 Aquila优化算法

S Gopi1, Prabhujit Mohapatra1

  • 1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.

Heliyon
|February 23, 2024

在PubMed 上查看摘要

概括
此摘要是机器生成的。

一个新的快速随机基于对立的学习鱼优化 (FROBLAO) 算法增强了群体智能. 这种新的方法克服了标准Aquila优化 (AO) 算法的局限性,改善了融合,避免了复杂问题的局部最佳.

关键词:
青 青 青 青 青 青 青快速随机的基于对立的学习.的元启发式算法.这就是OBL OBL.基于对立的学习是基于对立的.优化算法的优化算法

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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
  • 群集情报 群集情报 群集情报

背景情况:

  • 超启发式算法对于解决复杂的优化问题至关重要.
  • 阿奎拉优化 (AO) 算法是一种基于群体的方法,面临着缓慢的融合和局部优化等挑战.
  • 开发高效的元启发算法仍然是一个活跃的研究领域.

研究的目的:

  • 通过将基于对立的快速随机学习 (FROBL) 与Aquila优化 (AO) 算法集成,引入一种新的混合算法FROBLAO.
  • 增强 AO 算法的融合速度和全球搜索能力.
  • 在复杂的优化任务中解决标准的AO算法的局限性.

主要方法:

  • 拟议的FROBLAO算法结合了鱼鸟的狩猎策略和基于对立的快速随机学习机制.
  • 使用标准测试套件进行性能评估:CEC 2005,CEC 2019和CEC 2020.
  • 在六个现实世界的工程优化问题上进行验证.
  • 统计分析包括Wilcoxon排名和,t测试和弗里德曼测试,以比较FROBLAO与其他算法.

主要成果:

  • 与标准的AO算法和其他竞争方法相比,FROBLAO表现出卓越的性能.
  • 该算法显示了改进的收率和减少陷入局部最佳状态的倾向.
  • 统计测试证实了FROBLAO在各种优化基准中的显著有效性.
  • 成功地应用于现实生活中的工程问题,突出其实际实用性.
  • 结论:

    • FROBLAO算法有效地克服了标准的AO算法的局限性,特别是在复杂的优化场景中.
    • 集成FROBL显著增强了优化过程,导致更好的解决方案.
    • FROBLAO代表了基于群体的元启发式优化技术的有前途的进步.