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How to compare performance of robust design optimization algorithms, including a novel method
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
2017 (English)In: Artificial intelligence for engineering design, analysis and manufacturing, ISSN 0890-0604, E-ISSN 1469-1760, Vol. 31, no 3, 286-297 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
CAMBRIDGE UNIV PRESS , 2017. Vol. 31, no 3, 286-297 p.
Keyword [en]
Optimization; Robust Design Optimization; Surrogate Models; Surrogate-Based Optimization
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:liu:diva-140057DOI: 10.1017/S089006041700018XISI: 000407467600007OAI: oai:DiVA.org:liu-140057DiVA: diva2:1136604
Available from: 2017-08-28 Created: 2017-08-28 Last updated: 2017-09-14

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The full text will be freely available from 2018-02-03 14:47
Available from 2018-02-03 14:47

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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Language
  • de-DE
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