Optimization algorithms for multi-objective problems with fuzzy data

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...

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Veröffentlicht in:2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) S. 194 - 201
Hauptverfasser: Bahri, Oumayma, Ben Amor, Nahla, El-Ghazali, Talbi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2014
Schlagworte:
Context
Linear programming
Optimization
Sociology
Uncertainty
Vectors
Vehicles
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Zusammenfassung: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.
DOI:10.1109/MCDM.2014.7007207