增强的秘书鸟优化算法与多策略融合和考希-高斯交叉
Xinle Wang1, Peijun Wei1, Yancang Li2
1School of Civil Engineering, Hebei University of Engineering, Handan, 056038, Hebei, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种改进的Secretary Bird优化算法 (UTFSBOA),它提高了趋同准确度,并避免了局部最佳. 这种新的算法在复杂的,高维度的优化问题中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 算法设计 算法设计
背景情况:
- 秘书鸟优化算法 (SBOA) 患有低收精度和局部优化.
- 现有的优化算法经常与高维和复杂的问题空间作斗争.
研究的目的:
- 提出一个改进的算法,UTFSBOA,解决SBOA的局限性.
- 为了增强全球勘探,平衡开发,并在优化中丰富解决方案多样性.
主要方法:
- 将多战略协作和Cauchy-Gaussian交叉集成到SBOA中.
- 引入基于非线性因子的自适应性定向搜索.
- 在哈里斯·霍克优化 (HHO) 引发的指数式衰减能量逃逸因子的整合.
主要成果:
- 在30D和100D场景中,UTFSBOA实现了比SBOA更高的81.18%和88.22%的收精度.
- 获得了CEC2022测试组中的12个复杂函数中的7个的最佳解决方案.
- 在真实世界的工程问题上表现出显著的改进 (高达91.3%).
结论:
- 多策略协同作用显著提高了高维复杂空间中的算法稳定性.
- UTFSBOA是面对受约束和离散优化挑战的有效解决方案.
- 提议的改进克服了原来的SBOA.的局限性.
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