Optimization algorithms for multi-objective problems with fuzzy data

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.
Type de document :
Communication dans un congrès
IEEE SSCI‘2014 Symposium Series on Computational Intelligence, Dec 2014, Orlando, United States. pp.194 - 201, 〈10.1109/MCDM.2014.7007207〉
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https://hal.inria.fr/hal-01107767
Contributeur : Talbi El-Ghazali <>
Soumis le : mercredi 21 janvier 2015 - 15:14:56
Dernière modification le : mardi 3 juillet 2018 - 11:29:50

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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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