HR: 16:15h
AN: H14A-02    [Abstracts]
TI: Incorporating Search History into the Dynamically Dimensioned Search (DDS) Optimization Algorithm
AU: * Tolson, B A
EM: btolson@uwaterloo.ca
AF: Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
AU: Craig, J R
EM: jrcraig@uwaterloo.ca
AF: Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
AU: Esfahani, M A
EM: esfahani@members.asce.org
AF: Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
AB: The Dynamically Dimensioned Search (DDS) algorithm (Tolson and Shoemaker, 2007) was recently introduced as a parsimonious, efficient and robust optimization algorithm for automatic calibration of environmental models. DDS was designed to find practical or high quality solutions to a model calibration problem within a reasonable computational timeframe rather than the globally optimal solution. The simple structure of the original DDS algorithm only stores and utilizes the best current solution to guide the search. Population-based global optimization algorithms maintain a population of typically good quality solutions to influence the search. In this research, we examine how to utilize the search history of DDS to improve algorithm performance while maintaining the parsimonious and algorithmically simple nature of the original DDS algorithm. The modification to the original DDS algorithm involves storing a subset of relatively high quality solutions previously identified in the search and selecting one solution from which to make the next perturbation in order to sample a new candidate solution. Both the function value and their proximity to one another in multi-dimensional parameter space influences the likelihood of selecting a particular solution to perturb. This approach is motivated by initial results showing that for the same total computational budget, DDS with multiple restarts can sometimes be more effective than one longer DDS optimization trial. The history-based revisions discussed above allow the algorithm to search more of the parameter subspace, thus exploiting the strength of the less- refined restart approach, but with a higher likelihood of success. Results will be presented for a relatively simple problem as well as a more complex, high-dimensional automatic calibration problem. Results will also be assessed for various computational budgets.
DE: 1846 Model calibration (3333)
DE: 1847 Modeling
DE: 1879 Watershed
SC: Hydrology [H]
MN: 2007 Fall Meeting