地质论评
地質論評
지질론평
GEOLOGICAL REVIEW
2015年
3期
570-578
,共9页
骆乾坤%吴剑锋%杨运%钱家忠
駱乾坤%吳劍鋒%楊運%錢傢忠
락건곤%오검봉%양운%전가충
地下水污染%监测网设计%随机多目标优化%遗传算法
地下水汙染%鑑測網設計%隨機多目標優化%遺傳算法
지하수오염%감측망설계%수궤다목표우화%유전산법
groundwater contamination%monitoring network design%probabilistic multi-objective optimization%genetic algorithm
基于野外实际含水层参数存在空间变异性的客观事实,研发概率 Pareto 遗传算法(Probabilistic Pareto genetic algorithm,PPGA),用于求解考虑含水层参数空间变异性下地下水污染监测网多目标优化设计问题。PPGA在ε-改进非劣支配遗传算法(epsilon-dominance non-dominated sorted genetic algorithm II,ε-NSGAII)的基础上通过添加概率择优排序和概率拥挤度技术,寻求考虑参数空间变异条件下地下水污染监测网模拟—优化耦合模型的 Pareto最优解。将优化结果与蒙特卡洛(Monte Carlo,MC)模拟分析结果进行对比,验证优化结果的可靠性。算例求解结果表明:在求解考虑参数空间变异性条件下地下水污染监测网多目标优化设计问题时,PPGA 优化所得 Pareto 最优解变异性小,可靠性高,可为决策者提供一系列稳定可靠的监测方案。
基于野外實際含水層參數存在空間變異性的客觀事實,研髮概率 Pareto 遺傳算法(Probabilistic Pareto genetic algorithm,PPGA),用于求解攷慮含水層參數空間變異性下地下水汙染鑑測網多目標優化設計問題。PPGA在ε-改進非劣支配遺傳算法(epsilon-dominance non-dominated sorted genetic algorithm II,ε-NSGAII)的基礎上通過添加概率擇優排序和概率擁擠度技術,尋求攷慮參數空間變異條件下地下水汙染鑑測網模擬—優化耦閤模型的 Pareto最優解。將優化結果與矇特卡洛(Monte Carlo,MC)模擬分析結果進行對比,驗證優化結果的可靠性。算例求解結果錶明:在求解攷慮參數空間變異性條件下地下水汙染鑑測網多目標優化設計問題時,PPGA 優化所得 Pareto 最優解變異性小,可靠性高,可為決策者提供一繫列穩定可靠的鑑測方案。
기우야외실제함수층삼수존재공간변이성적객관사실,연발개솔 Pareto 유전산법(Probabilistic Pareto genetic algorithm,PPGA),용우구해고필함수층삼수공간변이성하지하수오염감측망다목표우화설계문제。PPGA재ε-개진비렬지배유전산법(epsilon-dominance non-dominated sorted genetic algorithm II,ε-NSGAII)적기출상통과첨가개솔택우배서화개솔옹제도기술,심구고필삼수공간변이조건하지하수오염감측망모의—우화우합모형적 Pareto최우해。장우화결과여몽특잡락(Monte Carlo,MC)모의분석결과진행대비,험증우화결과적가고성。산례구해결과표명:재구해고필삼수공간변이성조건하지하수오염감측망다목표우화설계문제시,PPGA 우화소득 Pareto 최우해변이성소,가고성고,가위결책자제공일계렬은정가고적감측방안。
Based on the fact that there is spatial variation of hydraulic conductivity,a new probabilistic Pareto genetic algorithm (PPGA ) is developed to solve multi-objective optimal design of groundwater contaminant monitoring network under the spatial variation of hydraulic conductivity.The PPGA is developed by adding the probabilistic Pareto domination ranking and probabilistic niche technique to the classic epsilon-dominance non-dominated sorted genetic algorithm II (ε-NSGAII ) to search for Pareto optimal solutions of multi-objective optimization problems under uncertainty.The Pareto optimal solutions are then compared with the MC analysis results to demonstrate the effectiveness and reliability of the PPGA.Comprehensive analysis demonstrates that the proposed PPGA can find Pareto-optimal solutions with low variability and high reliability and can provide a range of reliable monitoring programs for decision makers under the spatial variation of hydraulic conductivity.