Hybrid genetic algorithm - Local search methods for solving groundwater source identification inverse problems

Identifying contaminant sources in groundwater is important for developing effective remediation strategies and identifying responsible parties in a contamination incident. Groundwater source identification problems require solution of an inverse problem. Gradient-based local optimization approaches are among the most popular approaches for solving these inverse problems. While these methods are sometimes appropriate, they are not effective for problems that contain several local minima and for problems where the decision space is highly discontinuous or convoluted. For these types of problems, heuristic global search approaches such as genetic algorithms (GAs) are more effective. But methods such as GAs are inefficient for fine-tuning solutions once a near global minimum is found. For problems that contain several local minima, a hybrid approach starting with a global method and then fine-tuning with a local method may be more attractive, especially if the decision space is reasonably well behaved near the solution. In this paper, we compare several popular optimization methods for solving a simple groundwater source identification problem and show that hybrid GA-local search (GA-LS) approaches are generally more effective than using stand alone versions of each method. Some variants of the GA-LS approaches are then implemented on a parallel supercomputer to solve a more complex three-dimensional problem.