河流系统的最佳废物负载分配基于一个新的多目标子优化算法
Shekoofeh Haghdoost1, Mohammad Hossein Niksokhan1, Mohammad G Zamani2
1Faculty of Environment, University of Tehran, Tehran, Iran.
Environmental science and pollution research international
|November 27, 2023
概括
一个新的优化模型,多目标优化算法 (MOCOA),优于现有的河流水质管理方法. MOCOA有效地分配废物负载,在更清洁的河流系统中在违规指数和不平等值方面取得更好的结果.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 计算智能是一种计算智能.
背景情况:
- 由于废物排放的增加和有限的自我清洁能力,河流水污染越来越令人担忧.
- 现有的方法,如非主导排序基因算法-II (NSGA-II),已在河水质量管理方面表现出有效性.
- 需要新的优化模型来应对复杂的废物负载分配挑战.
研究的目的:
- 引入和评估一个新的优化框架,即多目标优化算法 (MOCOA),用于河流水质管理.
- 在废物负载分配问题中,比较MOCOA与NSGA-II的性能.
- 评估MOCOA在平衡排放目标与环境保护方面的能力.
主要方法:
- 开发了一个新的优化框架,包括非主导排序和排名选择.
- 实施了MOCOA对一个点源废物负载分配问题在Jajrood河,伊朗.
- 将一个模拟模型与一个混合优化模型 (Cuckoo优化算法+NSGA-II) 联系起来,创建一个多目标算法.
主要成果:
- 与NSGA-II相比,MOCOA在违规指数和不平等值方面表现出更高的帕雷托前线.
- 对于相同的种群大小,MOCOA比NSGA-II (1.31-2.36) 取得了明显更好的帕雷托前端范围 (0.379-2.28).
- MOCOA在人口规模较小的情况下实现了最佳股权,这表明效率更高.
结论:
- MOCOA是一种有效和高效的算法,用于河流系统中的废物负载分配.
- 拟议的MOCOA框架为优化河流水质提供了与NSGA-II相比显著的优势.
- 这项研究为环境当局在管理河流污染方面提供了宝贵的工具.
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