Stochastic Multi-objective Optimization on a Budget: Application to multi-pass wire drawing with quantified uncertainties

@article{Pandita2017StochasticMO,
  title={Stochastic Multi-objective Optimization on a Budget: Application to multi-pass wire drawing with quantified uncertainties},
  author={Piyush Pandita and Ilias Bilionis and Jitesh H. Panchal and B. P. Gautham and Amol Joshi and Pramod Zagade},
  journal={arXiv: Optimization and Control},
  year={2017}
}
Design optimization of engineering systems with multiple competing objectives is a painstakingly tedious process especially when the objective functions are expensive-to-evaluate computer codes with parametric uncertainties. The effectiveness of the state-of-the-art techniques is greatly diminished because they require a large number of objective evaluations, which makes them impractical for problems of the above kind. Bayesian global optimization (BGO), has managed to deal with these… 
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References

SHOWING 1-10 OF 34 REFERENCES

Extending Expected Improvement for High-dimensional Stochastic Optimization of Expensive Black-Box Functions

This work extends the expected improvement IAF to the case of design optimization under uncertainty wherein the EI policy is reformulated to filter out parametric and measurement uncertainties and employs a fully Bayesian interpretation of Gaussian processes by constructing a particle approximation of the posterior of its hyperparameters using adaptive Markov chain Monte Carlo.

Improvement criteria for constraint handling and multiobjective optimization

This thesis investigates efficient improvement criteria to address constrained problems and multiobjective problems, leading to the development of an improvement criterion that better balances improvement of the objective and all the constraint approximations.

Efficient Global Optimization of Expensive Black-Box Functions

This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

A Bayesian Approach to Constrained Multi-objective Optimization

This paper makes use of an extended domination rule taking both constraints and objectives into account under a unified multi-objective framework and proposes a generalization of the expected improvement sampling criterion adapted to the problem.

ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

Results show that NSGA-II, a popular multiobjective evolutionary algorithm, performs well compared with random search, even within the restricted number of evaluations used.

Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models

The proposed method is based on a kriging meta-model that provides a global prediction of the objective values and a measure of prediction uncertainty at every point and has excellent consistency and efficiency in finding global optimal solutions.

The computation of the expected improvement in dominated hypervolume of Pareto front approximations

A Monte Carlo method is reported that provides a direct computation procedure for the integral expression of the hypervolume measure and will be useful to enhance both accuracy and speed of computation for this important measure.

Survey of multi-objective optimization methods for engineering

A survey of current continuous nonlinear multi-objective optimization concepts and methods finds that no single approach is superior and depends on the type of information provided in the problem, the user's preferences, the solution requirements, and the availability of software.

Statistical Improvement Criteria for Use in Multiobjective Design Optimization

The work presented combines design of experiment methods with kriging (Gaussian process) models to enable the parallel evolution of multiobjective Pareto sets through the use of updating schemes based on new extensions of the expected improvement criterion commonly applied in single-objective searches.