增强的内核搜索算法,以优化本地搜索功能,并将其应用于碳纤维草稿流程
Ruyi Dong1, Ran Cui1, Zhennao Cai2
1College of Information and Control Engineering, Jilin University of Chemical Technology, Jilin, China.
PloS one
|November 26, 2025
概括
一个新的大型本地搜索内核搜索优化 (LLSKSO) 算法提高了本地搜索的准确性. LLSKSO提高了优化性能,并在基准测试和工程应用中显示出卓越的结果.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 超听证学是一种超听证学.
背景情况:
- 内核搜索优化 (KSO) 的局部搜索准确性有限,阻碍了有效的局部优化.
- 现有的优化方法往往难以平衡本地利用和全球探索.
研究的目的:
- 介绍一个新的大型本地搜索内核搜索优化 (LLSKSO) 算法.
- 提高KSO的本地搜索能力和整体性能.
主要方法:
- 使用好点设置机制,使初始群体均化.
- 集成Dung Beetle Optimizer (DBO) 的搜索机制以提升本地搜索.
- 采用Cauchy-Gaussian突变来逃避局部最佳并保持局部和全球搜索之间的平衡.
主要成果:
- 在50个基准函数中,LLSKSO在10个已建立的算法中表现出卓越的性能.
- 该算法在平均值,最佳值和方差方面取得了更好的统计结果.
- 在20个高维度函数中,LLSKSO在16个函数中实现了理论上的最佳状态,将CPU运行时间减少了30%.
结论:
- LLSKSO有效地提高了本地优化能力,并在本地和全球搜索之间实现了动态平衡.
- 该算法在工程应用中显示了显著的改进,例如碳纤维起草比率优化.
- LLSKSO是一个强大而高效的优化算法,适合复杂的工程问题.
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