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A Multiobjective Evolutionary Algorithm for Deriving Final Ranking from a Fuzzy Outranking Relation

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Evolutionary Multi-Criterion Optimization (EMO 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3410))

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Abstract

The multiple criteria aggregation methods allow us to construct a recommendation from a set of alternatives based on the preferences of a decision maker. In some approaches, the recommendation is immediately deduced from the preferences aggregation process. When the aggregation model of preferences is based on the outranking approach, a special treatment is required, but some non-rational violations of the explicit global model of preferences could happen. In this case, the exploitation phase could then be treated as a multiobjective optimization problem. In this paper a new multiobjective evolutionary algorithm, which allows exploiting a known fuzzy outranking relation, is introduced with the purpose of constructing a recommendation for ranking problems. The performance of our algorithm is evaluated on a set of test problems. Computational results show that the multiobjective genetic algorithm-based heuristic is capable of producing high-quality recommendations.

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References

  1. Brans, J.P., Vincke, P.: A Preference Ranking Organization Method. Management Science 31, 647–656 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bouyssou, D., Vincke, P.: Ranking Alternatives on the Basis of Preference Relations: A Progress Report with Special Emphasis on Outranking Relations. Technical reportIS-MG 95/03. Institut de Statistique et de Recherche Opérationnelle, Universite Libre de Bruxelles. Serie: Mathématiques de la Gestion (1995)

    Google Scholar 

  3. Coello, C.A.: A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowledge and Information Systems 1, 269–308 (1999)

    Google Scholar 

  4. Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002)

    MATH  Google Scholar 

  5. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley and Son, Chichester (2001)

    MATH  Google Scholar 

  6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  7. Fernandez, E., Leyva López, J.C.: A method based on multiobjective optimization for deriving a ranking from a fuzzy preference relation. European Journal of Operational Research 154, 110–124 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  8. Fodor, J., Roubens, M.: Fuzzy Preference Modeling and Multicriteria Decision Support. Kluwer, Dordrecht (1994)

    Google Scholar 

  9. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. John Wiley, New York (1966)

    MATH  Google Scholar 

  10. French, S.: Decision Theory: An Introduction to the Mathematics of Rationality. Halsted Press, New York (1986)

    MATH  Google Scholar 

  11. Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: Genetic Algorithms: Proceedings of the Fifth International Conference, pp. 416–423. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  12. Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithm in Multiobjective Optimization. Evolutionary Computation 3, 1–16 (1995)

    Article  Google Scholar 

  13. Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  14. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  15. Horn, J.: Multicriterion Decision Making. In: Back, T., Fogel, D.B., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation. IOP Publishing Ltd and Oxford University Press, Bristol (1997)

    Google Scholar 

  16. Horn, J., Nafploitis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Michalewicz, Z. (ed.) Proceeding of the First IEEE Conference on Evolutionary Computation, pp. 82–87. IEEE Service Center, Piscataway (1994)

    Chapter  Google Scholar 

  17. Keeney, R., Raiffa, H.: Decision with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York (1976)

    Google Scholar 

  18. Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front using the Pareto Archived Evolution Strategy. Evolutionary Computation 8, 149–172 (2000)

    Article  Google Scholar 

  19. Leyva-López, J.C., Fernández-González, E.: A Genetic Algorithm for Deriving Final Ranking from a Fuzzy Outranking Relation. Foundations of Computing and Decision Sciences 24, 33–47 (1999)

    Google Scholar 

  20. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  21. Ordoñez Reinoso, G., Valenzuela Rendón, M.: Permutation Optimization with Genetic Algorithms: The Traveling Salesman Problem (in Spanish). In: Proc. of the 3th. Latin-American Congress of Artificial Intelligence, pp. 271–282 (1992)

    Google Scholar 

  22. Orlovski, S.A.: Decision-Making with a Fuzzy Preference Relation. Fuzzy Sets and Systems 1, 155–167 (1978)

    Article  MathSciNet  Google Scholar 

  23. Poon, P.W., Carter, J.N.: Genetic Algorithm Crossover Operators for Ordering Applications. Computers & Operations Research 22, 135–147 (1995)

    Article  MATH  Google Scholar 

  24. Roy, B.: Multicriteria Methodology for Decision Aiding. Kluwer, Dordrecht (1996)

    MATH  Google Scholar 

  25. Roy, B.: The Outranking Approach and the Foundations of ELECTRE Methods. In: Bana e Costa, C.A. (ed.) Reading in Multiple Criteria Decision Aid, pp. 155–183. Springer, Berlin (1990)

    Google Scholar 

  26. Roy, B.: Decision-Aid and Decision-Making. European Journal of Operational Research 45, 324–331 (1990)

    Article  MathSciNet  Google Scholar 

  27. Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Grefenstette, J.J. (ed.) Genetic algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, Hillsdale, New Jersey, pp. 93–100 (1985)

    Google Scholar 

  28. Schwefel, H.P.: Numerical Optimization of Computer models. Wiley, Chichester (1981)

    MATH  Google Scholar 

  29. Srinivas, N., Deb, K.: Multiobjective Function Optimization using Nondominated Sorting Genetic Algorithms. Evolutionary Computation 2, 221–248 (1995)

    Article  Google Scholar 

  30. Triantaphyllou, E.: Multicriteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers, Boston (2000)

    Google Scholar 

  31. Vanderpooten, D.: The Construction of Prescriptions in Outranking Methods. In: Bana e Costa, C.A. (ed.) Reading in Multiple Criteria Decision Aid, pp. 184–215. Springer, Berlin (1990)

    Google Scholar 

  32. Van Veldhuizen, D.A., Lamont, G.A.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation 8, 1–26 (2000)

    Article  Google Scholar 

  33. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universidad de Occidente, Carr. a Culiacancito Km. 1.5, Culiacan, Sinaloa, 80020, Mexico

    Juan Carlos Leyva-Lopez

  2. Universidad Autonoma de Sinaloa, Prol. Josefa O. de Dominguez s/n, Ciudad Universitaria, Culiacan, Sinaloa, 80040, Mexico

    Juan Carlos Leyva-Lopez & Miguel Angel Aguilera-Contreras

Authors
  1. Juan Carlos Leyva-Lopez
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  2. Miguel Angel Aguilera-Contreras
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Editor information

Editors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), 07300, México, D.F., México

    Carlos A. Coello Coello

  2. Centro de Investigación en Matemáticas (CIMAT), A.P. 402, Guanajuato, 36000, Mexico

    Arturo Hernández Aguirre

  3. Computer Engineering and Networks Laboratory, ETH, Zurich, Switzerland

    Eckart Zitzler

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© 2005 Springer-Verlag Berlin Heidelberg

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Leyva-Lopez, J.C., Aguilera-Contreras, M.A. (2005). A Multiobjective Evolutionary Algorithm for Deriving Final Ranking from a Fuzzy Outranking Relation. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_17

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  • DOI: https://doi.org/10.1007/978-3-540-31880-4_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24983-2

  • Online ISBN: 978-3-540-31880-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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