Efficient Global Optimization Under Conditions of Noise and Uncertainty - A Multi-Model Multi-Grid Windowing Approach
- Sandia National Laboratories
Incomplete convergence in numerical simulation such as computational physics simulations and/or Monte Carlo simulations can enter into the calculation of the objective function in an optimization problem, producing noise, bias, and topo- graphical inaccuracy in the objective function. These affect accuracy and convergence rate in the optimization problem. This paper is concerned with global searching of a diverse parameter space, graduating to accelerated local convergence to a (hopefully) global optimum, in a framework that acknowledges convergence uncertainty and manages model resolu- tion to efficiently reduce uncertainty in the final optimum. In its own right, the global-to-local optimization engine employed here (devised for noise tolerance) performs better than other classical and contemporary optimization approaches tried individually and in combination on the "industrial" test problem to be presented.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 7256
- Report Number(s):
- SAND99-1244C; ON: DE00007256
- Country of Publication:
- United States
- Language:
- English
Similar Records
Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs
Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations