Hybrid optimization schemes for simulation-based problems.

Conference · Sat May 01 04:00:00 EDT 2010

OSTI ID:1017030

Fowler, Katie ^[1]; Gray, Genetha Anne; Griffin, Joshua D ^[2]

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1. Clarkson University, NY
2. SAS Institute, NC

The inclusion of computer simulations in the study and design of complex engineering systems has created a need for efficient approaches to simulation-based optimization. For example, in water resources management problems, optimization problems regularly consist of objective functions and constraints that rely on output from a PDE-based simulator. Various assumptions can be made to simplify either the objective function or the physical system so that gradient-based methods apply, however the incorporation of realistic objection functions can be accomplished given the availability of derivative-free optimization methods. A wide variety of derivative-free methods exist and each method has both advantages and disadvantages. Therefore, to address such problems, we propose a hybrid approach, which allows the combining of beneficial elements of multiple methods in order to more efficiently search the design space. Specifically, in this paper, we illustrate the capabilities of two novel algorithms; one which hybridizes pattern search optimization with Gaussian Process emulation and the other which hybridizes pattern search and a genetic algorithm. We describe the hybrid methods and give some numerical results for a hydrological application which illustrate that the hybrids find an optimal solution under conditions for which traditional optimal search methods fail.

• Research Organization: Sandia National Laboratories
• Sponsoring Organization: USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1017030
• Report Number(s): SAND2010-3383C
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
COMPUTERIZED SIMULATION
DESIGN
GAUSSIAN PROCESSES
GENETICS
OPTIMIZATION
PATTERN RECOGNITION