一个基于莱维飞行的混乱黑翼风算法,用于解决优化问题.
Taybe Alabed1,1, Sema Servi2
1Selcuk University, Konya, Turkey.
Scientific reports
|October 3, 2025
概括
增强的黑翼算法 (BKA) 和其变体,特别是CLBKA,在复杂的优化任务中表现出卓越的性能. 对于工程设计问题,CLBKA显著提高了融合,解决方案的准确性和稳定性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 黑翼算法 (BKA) 是一个生物灵感的优化元启发式算法.
- 现有的BKA变种在复杂的优化场景中扎,需要在勘探和开发之间保持平衡.
研究的目的:
- 引入黑翼算法 (BKA) 的改进变体,以提高复杂的优化.
- 评估这些新算法的性能与标准基准和现实世界的工程问题.
主要方法:
- 开发了三种修改后的BKA变体:CBKA (混乱映射),LBKA (莱维飞行) 和CLBKA (组合机制).
- 对23个标准基准函数 (单模,多模,固定维度) 的算法进行评估.
- 验证了六个工程设计问题的性能,并使用弗里德曼和威尔科克森的签名等级测试进行统计分析.
主要成果:
- 在23个基准函数中,CLBKA在20个方面实现了全球最佳.
- 在弗里德曼测试中,CLBKA排名第一 (平均排名为2.9348),并且表现优于基线BKA和其他元启证.
- 在工程设计问题上,CLBKA展示了卓越的解决方案准确性,融合速度和稳定性.
结论:
- 拟议的混合战略显著提高了BKA的搜索效率.
- 对于复杂的,受约束的优化任务,CLBKA是一种可靠和多功能优化器.
- 增强的BKA变种显示出在解决科学和工程领域具有挑战性的优化问题的巨大潜力.
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