Possibilistic framework for multi-objective optimization under uncertainty

Abstract : Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address combinatorial multi-objective problem under uncertainty using the possibilistic framework. To this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.
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Chapitre d'ouvrage
Recent Developments in Metaheuristics, 62, Springer, pp.27-42, 2017, Operations Research/Computer Science Interfaces Series - ORCS, 978-3-319-58252-8. 〈10.1007/978-3-319-58253-5_2〉
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https://hal.inria.fr/hal-01654714
Contributeur : Talbi El-Ghazali <>
Soumis le : lundi 4 décembre 2017 - 12:14:43
Dernière modification le : mardi 3 juillet 2018 - 11:29:06

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Oumayma Bahri, Nahla Ben Amor, El-Ghazali Talbi. Possibilistic framework for multi-objective optimization under uncertainty. Recent Developments in Metaheuristics, 62, Springer, pp.27-42, 2017, Operations Research/Computer Science Interfaces Series - ORCS, 978-3-319-58252-8. 〈10.1007/978-3-319-58253-5_2〉. 〈hal-01654714〉

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