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Simulation and Optimization as Modular Tasks within a Framework on High-Performance-Computing-Platforms
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Datum
2014
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BIS-Verlag
Zusammenfassung
In the context of the project „Effizienter Flughafen 2030“ separate, autarkic models simulating the
different processes at an airport have been coupled to build an integrative over-all model. For
scenario analysis and to identify the relevant model parameters for the over-all behavior it was
postulated to fit input parameter values automatically with regard to a certain optimal behavior of
the coupled system. To solve this task, a framework approach had been developed using softcomputing
optimization methods. Independently of its original motivation and application, this
approach can be applied to all complex optimization problems, if an encapsulated specification of
the model components and the optimization is provided. The soft computing approach has the
advantage to be able to deal easily with typical phenomena known in the area of environmental
modeling such as lack of definition, non-linearity, or incomplete data. On the technical level it was
a quite natural step to transfer the concept of the model coupling to the complete framework
including the optimization component and to implement the complete functionality as a web
service. For the prototype introduced in this paper genetic algorithms are selected for the
optimization module. The architecture of the genetic solver demonstrates the general framework
approach and is able to use high-performance-computing platforms to produce considerable
speedups without demanding high bandwidths or low latency interconnections for the platform
used.
A simple application example will demonstrate the interfaces for the model and the genetic solver
that have to be provided to encapsulate these components for usage in the framework. The outlook
deals with the optimization of the parameters of the genetic solver itself and proposes to interpret
the black-box web-service interface of the solver as a model itself such cascading two genetic
solvers: the inner one to optimize the simulation model parameters and the outer one to optimize
the genetic algorithm parameters of the inner one.