逃脱优化算法与工程和深度学习中的应用.
1Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
Scientific reports
|July 21, 2025
概括
新的Hare Escape Optimization (HEO) 算法通过平衡探索和利用来提高元启发性能. 在工程设计和深度学习应用中,HEO显示出卓越的结果.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 大自然启发的计算
背景情况:
- 在解决复杂的优化问题时,Metaheuristic算法至关重要.
- 现有的算法经常在平衡探索和利用方面扎,导致局部最佳.
- 需要新的方法来提高融合速度和解决方案质量.
研究的目的:
- 介绍子逃生优化 (HEO) 算法,这是一个由子逃避策略启发的新型元启发.
- 在基准函数和现实世界的工程问题上对HEO的性能进行评估,并与最新的算法对比.
- 证明HEO在优化卷积神经网络 (CNN) 的超参数方面的有效性.
主要方法:
- HEO 集成了 Levy 飞行动力学和自适应性方向转移,以改善勘探和开发的平衡.
- 在CEC 2015和CEC 2020测试台中的43个基准函数上测试了HEO.
- 将HEO应用于四个受约束的工程设计问题和CNN超参数优化.
主要成果:
- 与其他29个metaheuristics相比,HEO在单模和多模基准函数上表现优异.
- 在工程设计优化中实现了更好的解决方案可行性和计算效率.
- 在图像分类中显著提高了CNN的准确性和融合速度.
结论:
- HEO是一个强大的和可适应的元启发式优化工具.
- 它独特的搜索机制为智能优化提供了新的视角.
- 在工程和深度学习领域的应用中,HEO显示出显著的前景.
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