DRIME:用于数值优化问题的分布式数据引导的RIME算法
Jinghao Yang1, Yuanyuan Shao2, Bin Fu2
1Metropolitan College, Boston University, Boston, MA 02215, USA.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
本研究介绍了分布式数据引导的RIME (DRIME) 算法,以改善全球勘探和优化人口多样性. DRIME 增强了信息交换,并平衡了勘探/开发,以在复杂问题上提供卓越的性能.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 群集情报 群集情报 群集情报
背景情况:
- RIME算法存在弱全球探索,有限的人口间信息交换和人口多样性不足的问题.
- 这些局限性阻碍了其在解决复杂优化问题的有效性.
研究的目的:
- 提出一种新的分布式数据导向RIME (DRIME) 算法,以克服原始RIME算法的局限性.
- 为了提高全球勘探,信息交换和优化人口多样性.
主要方法:
- 引入了数据分布驱动的指导学习策略,以改善人口间的沟通,并指导人口利用/探索.
- 实施了一个软边缘搜索阶段,使用加权平均来平衡开发和勘探.
- 利用候选人池来取代硬边穿孔机制的最佳参考点,增加人口多样性并减少局部最佳风险.
主要成果:
- 在2017年CEC和2022年CEC测试集中,DRIME与几个最先进的算法相比取得了更高的性能,由竞争性弗里德曼排名证明了这一点.
- 在工程约束优化问题上表现出色,平均排名为1.23.
- 参数灵敏度和战略有效性分析证实了算法的稳定性及其新型组件的有效性.
结论:
- 拟议的DRIME算法显著提高了RIME算法的全球探索,信息交换和人口多样性的能力.
- DRIME 具有强大的搜索能力,并为广泛的优化挑战提供有效的解决方案.
- 这些发现表明DRIME在群集智能和优化算法设计方面是一个有希望的进步.
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