一种基于模糊集群的全球优化方法Kriging用于有氧废水处理
Yaohui Li1, Shuting Wang2, Yizhong Wu2
1College of Mechanical and Electrical Engineering, Xuchang University, Xuchang, 461000, Henan, China; School of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
Journal of environmental management
|April 2, 2025
概括
使用 Fuzzy Clustering Kriging (GO-FCK) 的新全球优化方法提高了复杂工程设计的效率. 它通过识别有前途的地区以实现专注的本地优化来最大限度地减少昂贵的评估.
科学领域:
- 工程优化工程优化
- 计算科学 计算科学
背景情况:
- 复杂的工程设计问题往往涉及计算上昂贵的黑子评估.
- 现有的Kriging辅助顺序优化方法难以平衡全球最佳性与准确性和评估最小化.
研究的目的:
- 提出一种新的全球优化方法 - - 模糊聚类Kriging (GO-FCK),以解决当前方法的局限性.
- 提高复杂工程设计优化的效率,稳定性和全球融合.
主要方法:
- 使用拉丁式超立方体设计 (LHD) 和代采样构建全球Kriging模型.
- 采用蒙特卡洛点的模糊集群,预测目标值较低,以确定有前途的地区.
- 在确定有前途的地区内开发本地Kriging模型,并使用预期改善 (EI) 进行优化.
主要成果:
- 与现有方法相比,GO-FCK方法显示出更高的优化效率和稳定性.
- 实现了有效的全球融合,满足了准确性要求,尽量减少了昂贵的评估.
- 在13个基准功能的成功应用和有氧废水处理模型验证了该方法的性能.
结论:
- 模糊集群Kriging (GO-FCK) 为计算密集型工程设计优化提供了一个有效的解决方案.
- 该方法成功地平衡了全球最佳性需求与有限昂贵评估的约束.
- GO-FCK为解决科学和工程领域复杂的优化挑战提供了强大而高效的方法.
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