Groundwater flow and particle tracking models are critical tools to simulate the natural system, contaminant fate and transport, and effects of remediation. Constrained optimization uses models to systematically explore the interplay between remedial design and contaminant fate, considering uncertainty. Sequential Linear Programming (SLP) provides a design alternative addressing a single goal (e.g. maximum hydraulic containment, maximum mass removal). Multi-objective algorithms like Nondominated Sorting Genetic Algorithm (NSGA-II) explore the tradeoffs among such objectives and more (e.g. cost, public-supply well contamination). We explore both approaches at a contaminated site in Long Island, New York USA. We compare the algorithms and ramifications on results. NSGA-II explores, at additional computational cost, explicit tradeoffs among multiple objectives, providing additional insights relative to SLP. The NGSA-II algorithm allows for graphical consideration of three objectives. SLP decision variables often settle at predetermined bounds. Bounds assignment thus differs from parameter estimation; bounds must be acceptable rather than safeguards.

中文翻译：

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比较用于调整地下水修复系统的单目标约束优化算法和多目标约束优化算法


地下水流和颗粒跟踪模型是模拟自然系统、污染物归宿和迁移以及修复效果的关键工具。考虑到不确定性，约束优化使用模型系统地探索补救设计和污染物命运之间的相互作用。顺序线性规划（SLP）提供了一种解决单一目标（例如最大水力遏制、最大质量去除）的设计替代方案。像非支配排序遗传算法（NSGA-II）这样的多目标算法探索了这些目标和更多目标（例如成本、公共供应井污染）之间的权衡。我们在美国纽约长岛的一个污染场地探索了这两种方法。我们比较算法和结果的影响。 NSGA-II 以额外的计算成本探索多个目标之间的明确权衡，提供与 SLP 相关的额外见解。 NGSA-II 算法允许以图形方式考虑三个目标。 SLP 决策变量通常固定在预定范围内。因此，界限分配不同于参数估计；界限必须是可接受的，而不是保障措施。