基于结合的自适应 ε 约束和多策略的储最佳运行研究改进了 Pelican 算法
Ji He1, Xiaoqi Guo1, Songlin Wang2
1School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, 450011, China.
这项研究介绍了一种改进的Pelican优化算法 (ε-IPOA),用于复杂的水库组操作. 新方法有效地降低了峰值洪水流量,增强了水库洪水控制策略.
科学领域:
- 优化算法的优化算法
- 水资源管理水资源的管理.
- 防洪工程 防洪工程 防洪工程
背景情况:
- 水库组运行是一个复杂的,多阶段的优化挑战.
- 现有的算法在洪水控制中与高维度,受约束的问题作斗争.
研究的目的:
- 开发一个增强的优化算法,以实现最佳的水库运行.
- 为了提高水水库的洪水控制效率.
主要方法:
- 将Pelican优化算法与自适应式 ε约束方法结合起来.
- 包括人口初始化,逆差演变和t分布扰动.
- 将改进的算法 (ε-IPOA) 应用于桑门和小朗迪水库的峰值切割模型.
主要成果:
- -IPOA算法表现出强大的优化能力和测试函数的稳定性能.
- 应用于布水库,e-IPOA在华宇安科控制点实现了44%的峰值剃须率.
- 有效地将峰值流量减少到12,319 m3/s,远低于22,000 m3/s的安全溢出极限.
- 超过了 ε-POA 和 ε-DE 算法,这些算法未能找到有效的解决方案.
结论:
- 拟议的 ε-IPOA 算法为优化级联水库洪水控制提供了一种新且有效的方法.
- 这种方法在管理复杂的水资源系统方面取得了重大进展,以提高安全性.
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