Hybrid Optimization Approaches for Solving Groundwater Inverse Problems in a Parallel Computing Environment
Mohamed Sayeed, G. Mahinthakumar · 2003
Solution to groundwater inverse problems is an important if not an essential step in developing efficient groundwater remediation and management strategies. But application of groundwater inverse modeling to field problems has been limited due to the lack of efficient and/or flexible algorithms and immense computational requirements. We have developed an efficient optimization based framework for solving three dimensional groundwater inverse problems in a parallel computing environment. Our implementation is based on a hybrid genetic algorithm - local search (GA-LS) optimizer that drives a parallel finite-element groundwater simulator. The MPI (Message Passing Interface) communication library is employed to exploit data parallelism within the groundwater simulator and task parallelism within the optimizer. Data parallelism in the groundwater simulator is achieved through a domain decomposition strategy and task parallelism in the optimizer is achieved through a dynamic self-scheduling algorithm. Two types of GAs, (i) binary/integer encoded GA (BGA/IGA), and (ii) real encoded GA (RGA), and three local search approaches, (i) Nelder-Mead simplex method (NMS), (ii) Hookes and Jeeves pattern search method (HKJ), and (iii) Powell's method of conjugate directions (PWL) have been implemented.