精英领袖矮人蒙古斯优化算法
Anping Lin1, Yong Liao2, Chengfeng Peng1
1School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou, 423000, People's Republic of China.
Scientific reports
|July 2, 2025
概括
增强的精英领袖矮人松鼠优化算法 (EL-DMOA) 通过解决不均的进化和多样性丧失来提高群体智能. 这种新的方法比现有方法更有效地优化复杂的问题.
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:
- 矮人蒙古斯优化算法 (DMOA) 是一种近期的元启发学,以解决复杂的优化问题而闻名.
- DMOA的一个局限性是它的轮盘选择,这可能导致不均的群体进化和快速的多样性丧失.
研究的目的:
- 提出一个新的两阶段结构精英领袖矮人蒙古斯优化算法 (EL-DMOA).
- 为了提高性能并克服原始DMOA的局限性.
主要方法:
- EL-DMOA引入了一种两阶段的方法:领导阶段和追随阶段.
- 领导阶段使用人工健身来选择领导者,并使用差异运算符来进行领导演变.
- 追随者阶段利用精英领袖指导群体,并结合交叉操作来保持多样性.
主要成果:
- 在CEC2017测试套件和现实问题上的实验证明了EL-DMOA的卓越性能.
- EL-DMOA的表现优于FIPS,DE/rand/1,以及其他四个近期的元启发.
- 不同运算符有效地提高了小群领袖的质量.
结论:
- 提出的EL-DMOA的两阶段结构促进了平衡和高效的群体进化.
- EL-DMOA有效地解决了DMOA的局限性,提高了其优化能力.
- 该算法显示了复杂的优化任务的巨大潜力.
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