Abstract
Re-Design for Robustness (RDR) represents a practical class of problems, where a limited set of components of an existing product are re-designed to improve the overall robustness of the product. RDR is still a common inefficient, expensive and a time consuming industry ritual, where component sensitivities are sequentially analyzed and altered with human experts in loop. In this paper, we introduce an automated approach, wherein a trade-off set of design variants (varying number of altered components) spanning the entire a range of feasibility and performance robustness are identified using a decomposition based evolutionary optimization algorithm. The benefits offered by the approach are highlighted using two re-design optimization problems from the automotive industry.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Beyer, H.G., Sendhoff, B.: Robust optimization - a comprehensive survey. Computer Methods in Applied Mechanics and Engineering 196(33–34), 3190–3218 (2007)
Asafuddoula, M., Singh, H., Ray, T.: Six-sigma robust design optimization using a many-objective decomposition based evolutionary algorithm. IEEE Transactions on Evolutionary Computation (99) (2014)
Das, I., Dennis, J.E.: Normal-bounday intersection: A new method for generating Pareto optimal points in multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)
Singh, H.K., Isaacs, A., Ray, T.: A Pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems. IEEE Transactions on Evolutionary Computation 15(4), 539–556 (2011)
Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley & Sons (2001)
Asafuddoula, M., Ray, T., Sarker, R., Alam, K.: An adaptive constraint handling approach embedded MOEA/D. In: IEEE World Congress on Computational Intelligence, (June 10–15 2012), 1–8
Sun, G., Li, G., Zhou, S., Li, H., Hou, S., Li, Q.: Crashworthiness design of vehicle by using multiobjective robust optimization. Structural and Multidisciplinary Optimization 44(1), 99–110 (2011)
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181, 1653–1669 (2006)
Gu, L., Yang, R.J., Tho, C.H., Makowskit, M., Faruquet, O., Li, Y.: Optimisation and robustness for crashworthiness of side impact. International Journal of Vehicle Design 26(4), 348–360 (2001)
Saxena, D.K., Deb, K.: Trading on infeasibility by exploiting constraint‘s criticality through multi-objectivization: A system design perspective. In: IEEE Congress on Evolutionary Computation, 911–926 (2007)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Singh, H., Asafuddoula, M., Alam, K., Ray, T. (2015). Re-design for Robustness: An Approach Based on Many Objective Optimization. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-15892-1_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15891-4
Online ISBN: 978-3-319-15892-1
eBook Packages: Computer ScienceComputer Science (R0)