Article2006

Efficient robust design via Monte Carlo sample reweighting

José R. Fonseca · Michael I. Friswell · Arthur William Lees

41 citations0/0 files storedInternational Journal for Numerical Methods in Engineering

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Probabilistic and Robust Engineering Design Geotechnical Engineering and Analysis Advanced Multi-Objective Optimization Algorithms

Abstract

Abstract A novel probabilistic method for the optimization of robust design problems is presented. The approach is based on an efficient variation of the Monte Carlo simulation method. By shifting most of the computational burden to outside of the optimization loop, optimum designs can be achieved efficiently and accurately. Furthermore by reweighting an initial set of samples the objective function and constraints become smooth functions of changes in the probability distribution of the parameters, rather than the stochastic functions obtained using a standard Monte Carlo method. The approach is demonstrated on a beam truss example, and the optimum designs are verified with regular Monte Carlo simulation. Copyright © 2006 John Wiley & Sons, Ltd.

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References

2017Structural Reliability Analysis and Prediction 1989Taguchi Techniques for Quality Engineering 1986Introduction to Quality Engineering: Designing Quality into Products and Processes 2001Reliability analysis—a review and some perspectives 1989Introduction to Quality Engineering: Designing Quality Into Products and Processes 2001Structural Reliability — Analysis and Prediction 1997Taguchi Techniques for Quality Engineering 2006Robust Design: An Overview

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