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关于多策略的研究改善了子搜索优化算法
Teng Fei1, Hongjun Wang1, Lanxue Liu1
1Institute of Information Engineering, Tianjin University of Commerce, Tianjin, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
本研究介绍了一种改进的搜索算法 (ISSA),以克服优化方面的局限性. 增强的算法展示了对复杂问题的卓越的融合,准确性和全球优化能力.
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:
- 标准的子搜索算法 (SSA) 受限于搜索空间,收速度缓慢,并倾向于陷入局部最佳状态.
- 这些局限性阻碍了其在解决复杂优化问题的有效性.
- 解决这些问题对于推进群体智能算法至关重要.
研究的目的:
- 开发一个多策略改进的子搜索算法 (ISSA),以提高SSA的性能.
- 提高全球勘探和逃离当地最佳条件的能力.
- 通过基准测试和工程应用来验证ISSA的有效性.
主要方法:
- 实施了人口动态调整策略,以调节发现者和加入者人口.
- 集成 honeypot优化算法的 (HBA) 更新策略,以完善连接器位置更新.
- 应用了扰动操作员和莱维飞行策略到发现者的最佳位置,以增强本地搜索.
- 使用13个基准函数对SSA和其他四个群集智能算法进行了ISSA的评估.
- 使用威尔科克森等级总和测试进行统计学意义分析.
主要成果:
- 与基线SSA和其他算法相比,ISSA表现出明显改善的融合速度和解决方案准确性.
- 展示了ISSA增强的全球勘探和本地搜索能力.
- 统计测试证实了ISSA的显著性能改善.
- 当ISSA应用于频道估计中的试点优化时,它实现了比特错误率的显著降低.
结论:
- 多策略改进的子搜索算法 (ISSA) 有效地解决了标准SSA的局限性.
- 在融合,准确性和全球优化方面,ISSA提供了卓越的性能.
- 该算法显示了实际工程应用的巨大潜力,特别是道估计.
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