Personal tools
Home → 10th International LS-DYNA Conference → Assessing the Convergence Properties of NSGA-II for Direct Crashworthiness Optimization

restrict search to current section
For a simple text search, enter your search term here. Multiple words may be found by combining them with AND and OR. The text in this field will be matched with items' contents, title and description.

 

Assessing the Convergence Properties of NSGA-II for Direct Crashworthiness Optimization

Contributors: Guangye Li - IBM Deep Computing Group, Houston TX, Tushar Goel, Nielen Stander - Livermore Software Technology Corporation

The elitist non-dominated sorting genetic algorithm (NSGA-II) converges to the Pareto optimal front (POF) if a sufficient number of function evaluations are allowed. However, for expensive problems involving crash simulations, only a limited number of simulations might be affordable. It is observed that initially there are significant advances towards the POF but as the population matures, the improvements are relatively small. This means that one can probably limit the computational expense by terminating the search at the right point. The paper also demonstrates a successful use of IBM cluster for parallel processing that significantly reduces clock time for optimization.

Optimization(1)-3.pdf — PDF document, 401Kb