A Solution Framework for Environmental Characterization Problems

This paper describes experiences developing a grid-enabled framework for solving environmental inverse problems. The solution approach taken here couples environmental simulation models with global search methods and requires readily available computational resources of the grid for computational tractability. The solution framework developed by the authors uses a master—worker strategy for task distribution and a pool for task mapping. Solution and computational performance results are presented for groundwater source identification and release history reconstruction problems. They indicate that high-quality solutions and significant raw performance improvements were attained for a deployment of the solution framework on the TeraGrid.

[1]  G. Mahinthakumar,et al.  Evolutionary algorithms-based parallel simulation-optimization framework for solving inverse problems , 2007 .

[2]  G. Mahinthakumar,et al.  Hybrid Genetic Algorithm—Local Search Methods for Solving Groundwater Source Identification Inverse Problems , 2005 .

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  R. Ababou,et al.  Implementation of the three‐dimensional turning bands random field generator , 1989 .

[5]  Joel H. Saltz,et al.  Application of Grid-enabled technologies for solving optimization problems in data-driven reservoir studies , 2004, Future Gener. Comput. Syst..

[6]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[7]  John Shalf,et al.  Classifying and enabling Grid Applications , 2003 .

[8]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[9]  Simon J. Cox,et al.  Developing services for design optimisation on the grid , 2004, IEEE International Conference onServices Computing, 2004. (SCC 2004). Proceedings. 2004.

[10]  Kai Xu,et al.  A scalable parallel genetic algorithm for x-ray spectroscopic analysis , 2005, GECCO '05.

[11]  Erick Cantú-Paz Designing efficient master-slave parallel genetic algorithms , 1997 .

[12]  Simon J. Cox,et al.  The GRID: Computational and data resource sharing in engineering optimisation and design search , 2001, Proceedings International Conference on Parallel Processing Workshops.

[13]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[14]  G. Mahinthakumar,et al.  A Hybrid Mpi-Openmp Implementation of an Implicit Finite-Element Code on Parallel Architectures , 2002, Int. J. High Perform. Comput. Appl..

[15]  Manish Parashar,et al.  Autonomic optimization of an oil reservoir using decentralized services , 2003, Proceedings of the International Workshop on Challenges of Large Applications in Distributed Environments, 2003..

[16]  G. Mahinthakumar,et al.  Implementation and Performance Analysis of a Parallel Multicomponent Groundwater Transport Code , 1999, PPSC.

[17]  E. C. Childs Dynamics of fluids in Porous Media , 1973 .

[18]  E. Poeter,et al.  Inverse Models: A Necessary Next Step in Ground‐Water Modeling , 1997 .

[19]  Bu-Sung Lee,et al.  Efficient Hierarchical Parallel Genetic Algorithms using Grid computing , 2007, Future Gener. Comput. Syst..

[20]  Enrique Alba,et al.  Improving flexibility and efficiency by adding parallelism to genetic algorithms , 2002, Stat. Comput..