红尾算法用于数值优化和现实世界的问题
Seydali Ferahtia1,2, Azeddine Houari1, Hegazy Rezk3
1Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France.
Scientific reports
|August 9, 2023
概括
一个新的红尾算法 (RTH) 模仿狩猎策略进行优化. 这种以自然为灵感的算法在解决基准,工程和复杂参数提取问题的方面表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 大自然启发的计算
背景情况:
- 在解决复杂问题时,Metaheuristic优化算法至关重要.
- 灵感来自自然的算法利用生物学策略来增强解决问题的能力.
- 现有的算法在某些应用程序的准确性,稳定性和融合速度方面面临挑战.
研究的目的:
- 介绍了一种新的以自然为灵感的元启发式优化算法,即红尾算法 (RTH).
- 模仿红尾的掠夺性狩猎策略,以应对现实世界的优化挑战.
- 评估RTH算法的性能与最近的最先进的算法相比.
主要方法:
- 红尾算法 (RTH) 是基于的三阶段狩猎过程开发的:高 (探索),低 (选择) 和俯卧/俯冲 (利用).
- 在三个问题类别上评估RTH性能:标准基准函数 (CEC2020,CEC2022),现实工程问题和质子交换膜燃料电池 (PEMFC) 参数提取.
- 对八种最近的算法进行了比较分析,包括FO,AVOA,MGO,GTO,COOT,HGS,AO和HHO.
主要成果:
- 对于大多数基准函数,RTH算法实现了最佳或接近最佳的解决方案,并实现了更快的融合.
- 与已公布的结果相比,在解决现实世界的工程问题和提取PEMFC参数方面表现出卓越的性能.
- 结果表明,在各种问题类型中,高准确度,稳定性和改进的融合速度.
结论:
- 拟议的红尾算法 (RTH) 显示出有效解决各种优化问题的巨大潜力.
- 由于其由自然狩猎行为启发的高效勘探和开发能力,RTH提供了竞争优势.
- 该算法的强大性能验证了它对复杂的工程和科学挑战的适用性.
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