Computational optimization strategies for the simulation of random media and components
AbstractIn 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
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Bibliographic InfoArticle provided by Springer in its journal Computational Optimization and Applications.
Volume (Year): 53 (2012)
Issue (Month): 3 (December)
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Web page: http://www.springer.com/math/journal/10589
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