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分数顺序增强的混合Young的双实验优化器用于托盘拓工程优化
Song Qin1, Junling Liu2, Xiaobo Bai1
1School of Art and Design, Xi'an University of Technology, Xi'an 710054, China.
Biomimetics (Basel, Switzerland)
|August 28, 2024
概括
一个新的分数顺序增强的混合Young's Double-Slit Experiment (YDSE) 优化器,FYDSE,增强了融合速度和全球搜索功能. 这种物理驱动的元启发式方法克服了复杂优化问题的原始YDSE的局限性.
科学领域:
- 计算智能是一种计算智能.
- 进行元启发式优化 (Meta-heuristic Optimization).
- 灵感来自物理的算法
背景情况:
- 的双实验 (YDSE) 优化算法是一种以物理为导向的元启发式,具有独特的搜索属性.
- YDSE面临着复杂,高维度问题的挑战,表现出缓慢的融合和局部最佳陷.
研究的目的:
- 为解决原始YDSE的局限性提出一个新的分数顺序增强混合YDSE (FYDSE).
- 为了提高YDSE优化器的融合率,全球搜索能力和复杂优化任务的稳定性.
主要方法:
- 在黑暗边缘的位置更新中引入了分数顺序策略,以实现高效的邻里搜索和减少局部最佳陷.
- 实施零碎的混乱映射,以获得更好的初始人口分布和更快的融合.
- 利用动态的反对策略来扩大勘探空间并改善全球最佳采购.
- 合并了一个垂直运营商,以平衡全球勘探和当地开采.
主要成果:
- 在91.6%的cec2022基准测试函数中,FYDSE表现优于标准YDSE.
- 在66.6%的测试中,FYDSE在所有比较算法中实现了最佳性能.
- 对于架结构优化问题 (20-bar, 24-bar, 72-bar),FYDSE获得了最佳最佳和平均重量,证明了其在困难情况下的有效性.
结论:
- 拟议的FYDSE有效地提高了YDSE算法的优化能力.
- 在FYDSE中,在融合速度,全球搜索和处理复杂的优化问题方面取得了显著的改进.
- FYDSE提供了一种有前途的方法来解决各种领域具有挑战性的优化任务.
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