You are here:
HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN
MATOTT, L. S., S. L. BARTELT-HUNT, A. J. RABIDEAU, AND K. R. FOWLER. HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN. Presented at SIAM Conference on Computational Science and Engineering, Costa Mesa, CA, February 19 - 23, 2007.
The primary goals are to: (1) Construct a 400-node PC-based supercomputing cluster supporting Windows and Linux computer operating systems (i.e. SuperMUSE: Supercomputer for Model Uncertainty and Sensitivity Evaluation); (2) Develop platform-independent system software for the management of SuperMUSE and parallelization of EPA models and modeling systems for implementation on SuperMUSE (and other PC-based clusters); (3) Conduct uncertainty and sensitivity analyses of the 3MRA modeling system; (4) Develop advanced algorithmic software for advanced statistical sampling methods, and screening, localized, and global sensitivity analyses; and (5) Provide customer-oriented model applications for probabilistic risk assessment supporting quality assurance in multimedia decision-making.
While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated annealing, and particle swarm optimization). Design problems were formulated as combinatorial optimizations where the sorptive layers of a landfill liner were selected to minimize contaminant transport. Results indicate that formal pre-tuning can improve algorithm performance and provide insight into the physical processes that control environmental systems.