动态淘金热优化器:融合工人适应和salp导航机制,以增强搜索功能
Yanhua Zhang1, Oluwatayomi Rereloluwa Adegboye2, Afi Kekeli Feda3
1Department of Physics and Electronic Engineering, Yuncheng University, Yuncheng City, Shanxi Province, China.
Scientific reports
|May 6, 2025
概括
新的动态淘金热优化器 (DGRO) 通过使用新的Salp导航和工人适应机制来改进原来的淘金热优化器 (GRO). 这种增强的算法在复杂的优化任务中实现了卓越的性能和稳定性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 淘金热优化器 (GRO) 面临着过早融合和次优化解决方案的挑战.
- 现有的优化方法往往难以有效地平衡勘探和开发.
研究的目的:
- 引入动态淘金热优化器 (DGRO),这是GRO的先进变体.
- 通过增强其勘探和开采能力来解决GRO的局限性.
- 在复杂的优化问题中,改进向全球最佳的趋同.
主要方法:
- 通过整合Salp导航机制 (SNM) 和工人适应机制 (WAM) 开发了DGRO.
- SNM为有效的解决方案提供了动态的人口指导,空间导航和顺利的勘探-开发过渡.
- 通过本地化互动,WAM增强了探索,在有前途的搜索区域促进了适应性学习.
主要成果:
- 在30维和50维空间中,DGRO在CEC2013和CEC2020基准函数中表现出卓越的性能和稳定性.
- 在七个复杂的工程优化问题上的实验结果证实了DGRO的有效性.
- 统计分析 (WRST,FRT) 验证了DGRO在现有方法方面的重大进展.
结论:
- 总监局有效地克服了GRO的局限性,特别是过早的趋同.
- 新的SNM和WAM机制显著提高了优化能力和稳定性.
- DGRO代表了优化算法领域的竞争和稳定的进步.
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