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2018 | OriginalPaper | Buchkapitel

Dealing with Epistemic Uncertainty in Multi-objective Optimization: A Survey

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Abstract

Multi-objective optimization under epistemic uncertainty is today present as an active research area reflecting reality of many practical applications. In this paper, we try to present and discuss relevant state-of-the-art related to multi-objective optimisation with uncertain-valued objective. In fact, we give an overview of approaches that have already been proposed in this context and limitations of each one of them. We also present recent researches developed for taking into account uncertainty in the Pareto optimality aspect.

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Literatur
1.
Zurück zum Zitat Barrico, C., Antunes, C.H.: Robustness analysis in multi-objective optimization using a degree of robustness concept. In: IEEE CEC, pp. 1887–1892 (2006) Barrico, C., Antunes, C.H.: Robustness analysis in multi-objective optimization using a degree of robustness concept. In: IEEE CEC, pp. 1887–1892 (2006)
3.
Zurück zum Zitat Bahri, O., Ben Amor N., Talbi E.-G.: Optimization algorithms for multi-objective problems with fuzzy data. In: IEEE International Symposium on MCDM, pp. 194–201 (2014) Bahri, O., Ben Amor N., Talbi E.-G.: Optimization algorithms for multi-objective problems with fuzzy data. In: IEEE International Symposium on MCDM, pp. 194–201 (2014)
4.
Zurück zum Zitat Binois, M., Ginsbourger, D., Roustant, O.: Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. Eur. J. Oper. Res. 243(2), 386–394 (2015)MathSciNetCrossRef Binois, M., Ginsbourger, D., Roustant, O.: Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. Eur. J. Oper. Res. 243(2), 386–394 (2015)MathSciNetCrossRef
5.
Zurück zum Zitat Basseur, M., Zitzler, E.: Handling uncertainty in indicator-based multiobjective optimization. Int. J. Comput. Intell. Res. 2(3), 255–272 (2006)MathSciNetCrossRef Basseur, M., Zitzler, E.: Handling uncertainty in indicator-based multiobjective optimization. Int. J. Comput. Intell. Res. 2(3), 255–272 (2006)MathSciNetCrossRef
7.
Zurück zum Zitat Sánchez, L., Couso, I., Casillas, J.: A multiobjective genetic fuzzy system with imprecise probability fitness for vague data. In: IEEE International Symposium on Evolving Fuzzy Systems, pp. 131–136 (2006) Sánchez, L., Couso, I., Casillas, J.: A multiobjective genetic fuzzy system with imprecise probability fitness for vague data. In: IEEE International Symposium on Evolving Fuzzy Systems, pp. 131–136 (2006)
8.
Zurück zum Zitat Goncalves, G., Hsu, T., Xu, J.: Vehicle routing problem with time windows and fuzzy demands: an approach based on the possibility theory. Int. J. Adv. Oper. Manage. 1(4), 312–330 (2009) Goncalves, G., Hsu, T., Xu, J.: Vehicle routing problem with time windows and fuzzy demands: an approach based on the possibility theory. Int. J. Adv. Oper. Manage. 1(4), 312–330 (2009)
11.
Zurück zum Zitat Fieldsend, J.E., Everson, R.M.: Multi-objective optimisation in the presence of uncertainty. In: IEEE CEC, vol. 1, pp. 243–250 (2005) Fieldsend, J.E., Everson, R.M.: Multi-objective optimisation in the presence of uncertainty. In: IEEE CEC, vol. 1, pp. 243–250 (2005)
12.
Zurück zum Zitat Goh, C.K., Tan, K.C.: Evolutionary multi-objective optimization in uncertain environments. J. Stud. Comput. Intell. 186, 5–18 (2009)MATH Goh, C.K., Tan, K.C.: Evolutionary multi-objective optimization in uncertain environments. J. Stud. Comput. Intell. 186, 5–18 (2009)MATH
14.
Zurück zum Zitat Haubelt, C., Teich, J.: Accelerating design space exploration using Pareto-front arithmetics. In: ACM Conference on Asia and South Pacific Design Automation, pp. 525–531 (2003) Haubelt, C., Teich, J.: Accelerating design space exploration using Pareto-front arithmetics. In: ACM Conference on Asia and South Pacific Design Automation, pp. 525–531 (2003)
15.
Zurück zum Zitat Hendriks, M., Geile, M., Basten, T.: Pareto analysis with uncertainty. In: 9th International Conference on EUC, pp. 189–196 (2011) Hendriks, M., Geile, M., Basten, T.: Pareto analysis with uncertainty. In: 9th International Conference on EUC, pp. 189–196 (2011)
18.
Zurück zum Zitat Limbourg, P., Aponte, D.E.S.: An optimization algorithm for imprecise multi-objective problem functions. In: IEEE CEC, vol. 1, pp. 459–466 (2005) Limbourg, P., Aponte, D.E.S.: An optimization algorithm for imprecise multi-objective problem functions. In: IEEE CEC, vol. 1, pp. 459–466 (2005)
19.
Zurück zum Zitat Liefooghe, A.: Methodes pour l’optimisation multiobjectif: Approche cooperative, prise en compte de l’incertitude et application logistique. PHD thesis, Universit de Lille 1, pp. 13–20 (2009) Liefooghe, A.: Methodes pour l’optimisation multiobjectif: Approche cooperative, prise en compte de l’incertitude et application logistique. PHD thesis, Universit de Lille 1, pp. 13–20 (2009)
20.
Zurück zum Zitat Liefooghe, A., Jourdan, L., Talbi, E.G.: Indicator-based approaches for multiobjective optimization in uncertain environments. In: 25th Mini-EURO Conference URPDM (2010) Liefooghe, A., Jourdan, L., Talbi, E.G.: Indicator-based approaches for multiobjective optimization in uncertain environments. In: 25th Mini-EURO Conference URPDM (2010)
21.
Zurück zum Zitat Meng, Z., Shen, R., Jiang, M.: An objective penalty functions algorithm for multiobjective optimization problem. J. Oper. Res. 1(4), 229 (2011) Meng, Z., Shen, R., Jiang, M.: An objective penalty functions algorithm for multiobjective optimization problem. J. Oper. Res. 1(4), 229 (2011)
22.
Zurück zum Zitat Petrone, G.: Optimization under Uncertainty: theory, algorithms and industrial applications. PHD thesis, Università degli Studi di Napoli Federico II, pp. 77–122 (2011) Petrone, G.: Optimization under Uncertainty: theory, algorithms and industrial applications. PHD thesis, Università degli Studi di Napoli Federico II, pp. 77–122 (2011)
23.
Zurück zum Zitat Talbi, E.-G.: Metaheuristics: From design to implementation, vol. 74, pp. 309–373. John Wiley and Sons (2009) Talbi, E.-G.: Metaheuristics: From design to implementation, vol. 74, pp. 309–373. John Wiley and Sons (2009)
25.
Zurück zum Zitat Sahinidis, N.V.: Optimization under uncertainty: state-of-the-art and opportunities. J. Comput. Chem. Eng. 28(6), 971–983 (2004)CrossRef Sahinidis, N.V.: Optimization under uncertainty: state-of-the-art and opportunities. J. Comput. Chem. Eng. 28(6), 971–983 (2004)CrossRef
26.
Zurück zum Zitat Saka, M.P., Dogan, E.: Recent developments in metaheuristic algorithms: a review. J. Comput. Technol. Rev. 5(4), 31–78 (2012)CrossRef Saka, M.P., Dogan, E.: Recent developments in metaheuristic algorithms: a review. J. Comput. Technol. Rev. 5(4), 31–78 (2012)CrossRef
27.
Zurück zum Zitat Silva, R.C., Yamakami, A.: Definition of fuzzy Pareto-optimality by using possibility theory. In: IFSA/EUSFLAT Conference, pp. 1234–1239. Citeseer (2009) Silva, R.C., Yamakami, A.: Definition of fuzzy Pareto-optimality by using possibility theory. In: IFSA/EUSFLAT Conference, pp. 1234–1239. Citeseer (2009)
28.
Zurück zum Zitat Wang, G., Huawei, J.: Fuzzy-dominance and its application in evolutionary many objective optimization. In: IEEE International Conference on Computational Intelligence and Security Workshops CISW, pp. 195–198 (2007) Wang, G., Huawei, J.: Fuzzy-dominance and its application in evolutionary many objective optimization. In: IEEE International Conference on Computational Intelligence and Security Workshops CISW, pp. 195–198 (2007)
29.
Zurück zum Zitat Zadeh, L.A.: Fuzzy sets. In: Fuzzy Sets, Fuzzy Logic and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh, pp. 394–432 (1996) Zadeh, L.A.: Fuzzy sets. In: Fuzzy Sets, Fuzzy Logic and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh, pp. 394–432 (1996)
30.
Zurück zum Zitat Zhou, J., Yang, F., Wang, K.: Multi-objective optimization in uncertain random environments. J. Fuzzy Optim. Decis. Mak. 13(4), 397–413 (2014)MathSciNetCrossRef Zhou, J., Yang, F., Wang, K.: Multi-objective optimization in uncertain random environments. J. Fuzzy Optim. Decis. Mak. 13(4), 397–413 (2014)MathSciNetCrossRef
Metadaten
Titel
Dealing with Epistemic Uncertainty in Multi-objective Optimization: A Survey
verfasst von
Oumayma Bahri
El-Ghazali Talbi
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-91479-4_22

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