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使用参数代更新基于战略的群优化算法进行地下水污染监测网络的最佳布局设计

Jiannan Luo^1,2,3, Yu Xiong^4,5,6, Zhuo Song^4,5,6

¹Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China. luojiannan01@126.com.

Environmental science and pollution research international

|October 20, 2023

概括

一个新的参数-代更新基于战略的群优化 (PIUSACO) 算法改进了地下水污染监测网络 (GPMN) 的设计. 这种方法提高了检测率,并加快了用于及时识别污染物的趋同.

科学领域:

环境科学环境科学
地质科学地质科学
计算机科学计算机科学

背景情况:

有效的地下水污染监测网络 (GPMN) 设计对于及时检测和修复至关重要.
传统的群优化 (ACO) 算法参数是固定的,可能会限制全球搜索和融合速度.
优化GPMN布局需要强大的模拟优化方法.

研究的目的:

为GPMN最佳布局设计提出和评估基于参数代更新策略的群优化 (PIUSACO) 算法.
开发一种使用遗传算法支持向量回归 (GA-SVR) 来减少计算负载的替代模型.
通过改进的GPMN设计,提高对地下水污染的及时检测.

主要方法:

使用PIUSACO算法开发了一个模拟优化框架,用于GPMN设计.
使用GA-SVR方法创建数字模拟的替代模型,考虑含水层参数的不确定性.
应用PIUSACO算法优化了GPMN布局在城市垃圾填埋场在BaiCheng城市,中国.

主要成果:

与传统的ACO和随机布局相比,PIUSACO算法设计的GPMN显示出明显更高的污染检测率.
与标准的ACO算法相比,PIUSACO算法显示出更好的全球搜索能力和加快的融合速度.
该GA-SVR替代模型有效地减少了设计过程中的计算负担.

结论:

PIUSACO算法是GPMN的最佳布局设计的可行和有效方法.
拟议的基于PIUSACO的GPMN设计有助于及时检测地下水污染事件.
将PIUSACO与GA-SVR集成,为复杂的环境监测网络优化提供了一种高效的方法.

关键词:

基因算法支持向量回归替代模型的基因算法.监测地下水污染的监测参数代更新基于战略的群优化模拟优化模拟优化不确定性分析不确定性分析

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