Computational optimization strategies for the simulation of random media and components

Edoardo Patelli, Gerhart I. Schuëller

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)903-931
Number of pages29
JournalComputational Optimization and Applications
Issue number3
Publication statusPublished - 9 Feb 2012


  • optimization techniques
  • parallel computing
  • random heterogeneous media
  • simulation
  • soft computing
  • super-elements
  • genetic algorithms
  • simulated annealing
  • finite element method


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