在元启发学中基于对立的学习技术:分类,比较和融合分析
Rihab Lakbichi1, Farouq Zitouni1, Saad Harous2
1Department of Computer Science and Information Technology, University of Kasdi Merbah, Laboratory of Artificial Intelligence and Information Technology, Ouargla, Algeria.
PeerJ. Computer science
|September 24, 2025
概括
基于对立的学习 (OBL) 增强了元启发算法 (MA). 准反射OBL在测试的MA中表现出优越的融合速度和溶液质量,优于其他OBL变体.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 基于对立的学习 (OBL) 是改进元启发算法 (MA) 的关键策略.
- 缺乏对OBL变体对MA性能影响的结构化分析.
- 现有的管理区经常遭受缓慢的融合,有限的勘探和勘探-开发不平衡.
研究的目的:
- 分类和分析九种不同的OBL技术.
- 系统地评估五个OBL变体的有效性,整合到五个MA中.
- 证明OBL能够增强面临常见优化挑战的MAs的能力.
主要方法:
- 在差异进化,遗传算法,粒子群优化,人工蜜蜂殖民地和和搜索中实施了五种OBL变体.
- 在初始化和生成更新阶段评估混合算法.
- 在CEC2022套件中的12个基准函数上测试算法,分析关键性能指标并使用弗里德曼测试进行统计验证.
主要成果:
- 准反射的基于对立的学习始终优于其他OBL变体.
- 增强的MA显示了大多数基准函数的更好的融合速度和解决方案质量.
- 统计验证证实了OBL增强MA变体之间的显著性能差异.
结论:
- 基于对立的学习显著提高了元启发算法性能.
- 准反射OBL是一种高效的变种,可以提高度和解决方案质量.
- 本研究提供了一个结构化的框架,用于理解和应用OBL在元启发性优化.
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