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Optimization algorithms for multi-objective problems with fuzzy data

Oumayma Bahri 1 Nahla Ben Amor 1 El-Ghazali Talbi 2, 3
1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
3 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : This paper addresses multi-objective problems with fuzzy data which are expressed by means of triangular fuzzy numbers. In our previous work, we have proposed a fuzzy Pareto approach for ranking the generated triangular-valued functions. Then, since the classical multi-objective optimization methods can only use crisp values, we have applied a defuzzification process. In this paper, we propose a fuzzy extension of two well-known multi-objective evolutionary algorithms: SPEA2 and NSGAII by integrating the fuzzy Pareto approach and by adapting their classical techniques of diversity preservation to the triangular fuzzy context. An application on multi-objective Vehicle Routing Problem (VRP) with uncertain demands is finally proposed and evaluated using some experimental tests.
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Conference papers
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https://hal.inria.fr/hal-01107767
Contributor : Talbi El-Ghazali <>
Submitted on : Wednesday, January 21, 2015 - 3:14:56 PM
Last modification on : Friday, December 11, 2020 - 6:44:04 PM

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CNRS | INRIA | CRISTAL-DOLPHIN | CRISTAL | UNIV-LILLE3 | UNIV-LILLE

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Oumayma Bahri, Nahla Ben Amor, El-Ghazali Talbi. Optimization algorithms for multi-objective problems with fuzzy data. IEEE SSCI‘2014 Symposium Series on Computational Intelligence, Dec 2014, Orlando, United States. pp.194 - 201, ⟨10.1109/MCDM.2014.7007207⟩. ⟨hal-01107767⟩

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