中文摘要：

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。

英文摘要：

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 nondominated 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.