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Sequential Parameter Optimization in Noisy Environments

  • Sequential Parameter Optimization is a model-based optimization methodology, which includes several techniques for handling uncertainty. Simple approaches such as sharp- ening and more sophisticated approaches such as optimal computing budget allocation are available. For many real world engineering problems, the objective function can be evaluated at different levels of fidelity. For instance, a CFD simulation might provide a very time consuming but accurate way to estimate the quality of a solution.The same solution could be evaluated based on simplified mathematical equations, leading to a cheaper but less accurate estimate. Combining these different levels of fidelity in a model-based optimization process is referred to as multi-fidelity optimization. This chapter describes uncertainty-handling techniques for meta-model based search heuristics in combination with multi-fidelity optimization. Co-Kriging is one power- ful method to correlate multiple sets of data from different levels of fidelity. For the first time, Sequential Parameter Optimization with co-Kriging is applied to noisy test functions. This study will introduce these techniques and discuss how they can be applied to real-world examples.

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Author:Thomas Bartz-BeielsteinGND, Christian Jung, Martin Zafferer
Series (Serial Number):CIplus (4/2015)
Document Type:Report
Year of Completion:2015
Release Date:2015/05/28
Tag:Co-Kriging; Kriging; Metamodel
Co-Kriging; Kriging; Metamodel
GND Keyword:Evolutionärer Algorithmus; Metaheuristik; Optimierung; Optimierungsproblem; Unsicherheit
Contributor:Thomas Bartz-Beielstein
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Informatik
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung