| Groundwater contamination is the result of multiple human activities, such as agriculture, industrial practices and military operations. The traditional remediation approach is to combine pump-and-treat for plume containment and contaminant capture, with other remediation technology for source control. Pump-and-treat systems are expensive, typically requiring high installation and operation costs. The traditional solution approach is then to proceed by trial-and-error, evaluating different design alternatives and selecting the best one from those evaluated. A large body of research has demonstrated that coupling optimization models with simulation models can aid in identifying effective remediation designs. The simple genetic algorithm (SGA) is a heuristic technique capable of solving these types of problems. Unfortunately, the solution of these complex problems is generally computationally intensive.; This research focuses on the development and use of a hybrid genetic algorithm (HGA), a method that combines the use of SGA with local search to solve a groundwater remediation problem. The inclusion of local search helps to speed up the solution process and to make the solution technique more robust. The result of this research is a highly reliable numerical tool, the enhanced self-adaptive hybrid genetic algorithm (e-SAHGA) to more efficiently and effectively solve problems using simple genetic algorithms (SGAs). With this tool, the designer can evaluate different solution alternatives in a more timely fashion. A step-by-step methodology has also been developed for evaluating the optimal parameters for using the algorithm. This methodology, together with the adaptive nature of the algorithm, reduces the need for trial-and-error experiments to determine the optimal set of parameters for the algorithm.; The application of the e-SAHGA algorithm to a hypothetical groundwater remediation design problem showed 90% reliability in identifying the solution faster than the SGA, with average savings of 64% across 100 runs with different random initial populations. Finally, e-SAHGA was tested on a field-scale remediation design problem, re-evaluation of the remediation system for Umatilla Army Depot, where it gave computational savings between 30% and 60% and, for one solution method, found a solution that was 4% better than the one found by the SGA. |
