Computational optimization strategies for the simulation of random media and components
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
Volume (Year): 53 (2012)
Issue (Month): 3 (December)
|Contact details of provider:|| Web page: http://www.springer.com|
|Order Information:||Web: http://www.springer.com/math/journal/10589|
When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:53:y:2012:i:3:p:903-931. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Rebekah McClure)
If references are entirely missing, you can add them using this form.