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How to compare performance of robust design optimization algorithms, including a novel method

Published online by Cambridge University Press:  03 August 2017

Johan A. Persson  and
Johan Ölvander
Johan A. Persson*
Affiliation:
Department of Management and Engineering, Linköping University, Linköping, Sweden
Johan Ölvander
Affiliation:
Department of Management and Engineering, Linköping University, Linköping, Sweden
*
Reprint requests to: Johan A. Persson, Department of Management and Engineering, Linköping University, Linköping SE-581 83, Sweden. E-mail: [email protected]
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Abstract

This paper proposes a method to compare the performances of different methods for robust design optimization of computationally demanding models. Its intended usage is to help the engineer to choose the optimization approach when faced with a robust optimization problem. This paper demonstrates the usage of the method to find the most appropriate robust design optimization method to solve an engineering problem. Five robust design optimization methods, including a novel method, are compared in the demonstration of the comparison method. Four of the five compared methods involve surrogate models to reduce the computational cost of performing robust design optimization. The five methods are used to optimize several mathematical functions that should be similar to the engineering problem. The methods are then used to optimize the engineering problem to confirm that the most suitable optimization method was identified. The performance metrics used are the mean value and standard deviation of the robust optimum as well as an index that combines the required number of simulations of the original model with the accuracy of the obtained solution. These measures represent the accuracy, robustness, and efficiency of the compared methods. The results of the comparison show that sequential robust optimization is the method with the best balance between accuracy and number of function evaluations. This is confirmed by the optimizations of the engineering problem. The comparison also shows that the novel method is better than its predecessor is.

Keywords

OptimizationRobust Design OptimizationSurrogate ModelsSurrogate-Based Optimization
Type
Special Issue Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 286 - 297
Copyright
Copyright © Cambridge University Press 2017 

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