一个增强的生长优化算法与前哨机制和早期停止战略,用于在狭窄的水库中优化生产
Chenglong Wang1,2, Chengqian Tan2,3, Youyou Cheng2,3
1College of Petroleum Engineering, Xi'an Shiyou University, Xi'an 710065, China.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
一个新的战略性生长优化 (SMGO) 算法通过提高水库工程中的融合速度和解决方案精度来增强碳化合物回收. 这种新的方法解决了狭窄的水库中复杂的优化挑战.
科学领域:
- 水库工程和地质学
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 优化对于复杂的水库地质和工程问题至关重要,特别是在增强碳化合物回收和管理地下不确定性方面.
- 摩斯生长优化 (MGO) 算法虽然是生物启发的,但在复杂的景观中面临着缓慢融合和局部优化的挑战.
研究的目的:
- 引入战略生长优化 (SMGO) 算法,旨在克服MGO的局限性.
- 增强水库开发和其他复杂的工程任务的全球优化能力.
主要方法:
- SMGO集成了一个前哨机制 (OM) 来改进本地搜索和一个早期停止战略 (ESS) 来恢复人口.
- 该算法的性能使用全球优化基准进行了评估,并与最先进的元启发方法进行了比较.
主要成果:
- 与现有方法相比,SMGO表现出优越或具有竞争力的性能,在准确性和稳定性方面显著改善.
- 该算法成功地确定了紧密水库的增强生产策略,从而提高了经济回报.
结论:
- 对于复杂的工程问题,SMGO提供了强大而高效的优化工具,特别是在紧密的水库开发的战略资源管理中.
- 提议的改进有效地加快了融合,并保持了解决方案的质量,使SMGO成为优化技术的宝贵进步.
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