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Possibilistic framework for multi-objective optimization under uncertainty

Oumayma Bahri 1, 2 Nahla Ben Amor 1 El-Ghazali Talbi 3, 2
1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
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.
Keywords : Multi-objective optimization Uncertainty Possibilty theory Evolutionary algorithms Vehicle routing problem
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https://hal.inria.fr/hal-01654714
Contributor : Talbi El-Ghazali <>
Submitted on : Monday, December 4, 2017 - 12:14:43 PM
Last modification on : Friday, December 11, 2020 - 6:44:05 PM

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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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