适应性多机制集成在全球优化和工程设计问题的猪优化器
Hairong Xie1, Jia Mao2, Xijun Wan1
1School of Transportation, Jilin University, Changchun, 130022, Jilin, China.
Scientific reports
|February 16, 2026
概括
本研究介绍了增强的猪优化器 (SDHCPO),以克服过早的趋同并改善优化中的多样性. 改进的算法在基准和工程问题上表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 工程应用 工程应用
背景情况:
- 顶猪优化器 (CPO) 在工程问题上表现有前途,但由于过早的融合和低种群多样性而受到影响.
- 现有的CPO限制阻碍了其在复杂,高维度优化任务中的有效性.
研究的目的:
- 为解决CPO的局限性提出一个增强的多机制形猪优化器 (SDHCPO).
- 改善全球勘探,本地开发和CPO算法的融合稳定性.
主要方法:
- 实施Sobol-Opposition-Based Learning (Sobol-OBL),以改善最初的人口分布.
- 集成的共弦结用于动态重量调整,以提高合稳定性.
- 整合了DE/rand/1和水平-垂直交叉战略,以防止过早的融合和维度停滞.
主要成果:
- 在CEC2017和CEC2022基准套件上,SDHCPO显著优于其他七种元启发算法.
- 改进的算法实现了卓越的全球勘探,本地开发准确性和融合稳定性.
- 关于工程设计问题的实证研究证实了SDHCPO的有效性,产生了最知名或极具竞争力的解决方案.
结论:
- 拟议的SDHCPO有效地解决了原始CPO的局限性,特别是过早的融合和不足的多样性.
- 由于其增强的性能,SDHCPO展示了复杂的现实世界工程优化任务的巨大潜力.
- 多机制增强策略有助于算法的稳定性和广泛应用.
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