Comparing single and multiple objective constrained optimization algorithms for tuning a groundwater remediation system
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.