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Computational optimization strategies for the simulation of random media and components

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  • Edoardo Patelli
  • Gerhart Schuëller

Abstract

In this paper efficient computational strategies are presented to speed-up the analysis of random media and components. In particular, a Hybrid Stochastic Optimization (HSO) tool, based on the synergy between various algorithms, i.e. Genetic Algorithms, Simulated Annealing as well as Tabu-list is suggested to reconstruct a set of microstructures starting from probabilistic descriptors. The subsequent analysis (e.g. Finite Element analysis) can be performed to obtain the desired macroscopic quantity of interest and, providing a link between the micro- and the macro-scale. Different computational speed-up strategies are also presented. The proposed simulation approach is highly parallelizable, flexible and scalable. It can be adopted by other fields as well where an optimization analysis is required and a set of different solutions should be identified in order to perform computational experiments. Numerical examples demonstrate the applicability of the proposed strategies for realistic problems. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Edoardo Patelli & Gerhart Schuëller, 2012. "Computational optimization strategies for the simulation of random media and components," Computational Optimization and Applications, Springer, vol. 53(3), pages 903-931, December.
    • Handle: RePEc:spr:coopap:v:53:y:2012:i:3:p:903-931
    DOI: 10.1007/s10589-012-9463-1
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    References listed on IDEAS

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    1. Marseguerra, M. & Zio, E. & Martorell, S., 2006. "Basics of genetic algorithms optimization for RAMS applications," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 977-991.
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