Abstract
Most approaches in the evolutionary multiobjective optimization literature concentrate mainly on generating an approximation of the Pareto front. However, this does not completely solve the problem since the Decision Maker (DM) still has to choose the best compromise solution out of that set. This task becomes difficult when the number of criteria increases. In this chapter, we introduce a new way to incorporate and update the DM’s preferences into a Multiobjective Evolutionary Algorithm, expressed in a set of solutions assigned to ordered categories. We propose a variant of the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II), called Hybrid-MultiCriteria Sorting Genetic Algorithm (H-MCSGA). In this algorithm, we strengthen the selective pressure based on dominance adding selective pressure based on assignments to categories. Particularly, we make selective pressure towards non-dominated solutions that belong to the best category. In instances with 9 objectives on the project portfolio problem, H-MCSGA outperforms NSGA-II obtaining non-dominated solutions that belong to the most preferred category.
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References
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms, pp. 13–46. Wiley, Chichester (2001). (Weinheim-Brisbane-Singapore-Toronto)
Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving MultiObjective Problems, 2nd edn. Springer, New York (2007)
Fernandez, E., Lopez, E., Bernal, S., Coello, C.A., Navarro, J.: Evolutionary multiobjective optimization using an outranking-based dominance generalization. Comput. Oper. Res. 37(2), 390–395 (2010)
Jaimes, A.L., Martinez, S.Z., Coello, C.A.: An introduction to multiobjective optimization techniques. Optim. Polym. Process. 29–57 (2009)
Miettinen, K.: Introduction to multiobjective optimization: noninteractive approaches. In: Branke, J., Deb, K., Miettinen, K., Slowinski, R. (eds.) Multiobjective Optimization: Interactive and Evolutionary Approaches, pp. 1–26. Springer, Berlin (2008)
Fernandez, E., Lopez, E., Lopez, F., Coello, C.: Increasing selective pressure toward the best compromise in evolutionary multiobjective optimization: the extended NOSGA method. Inf. Sci. 181, 44–56 (2011)
Wang, Y., Yang, Y.: Particle swarm optimization with preference order ranking for multi-objective optimization. Inf. Sci. 179(12), 1944–1959 (2009)
Deb, K., Chaudhuri S., Miettinen, K.: Towards estimating nadir objective vector using evolutionary approaches. In: Proceedings of the 8th Genetic and Evolutionary Computation COnference (GECCO’O6), pp. 643–650. (2006)
Bechikh, S.: Incorporating decision maker’s preference information in evolutionary multi-objective optimization. Dissertation Ph.D. thesis, High Institute of Management of Tunis, University of Tunis, Tunisia. http://delta.cs.cinvestav.mx/~ccoello/EMOO/thesis-bechikh.pdf.gz (2013)
Carazo, A., Gomez, T., Molina, J., Hernandez-Diaz, A., Guerrero, F., Caballero, R.: Solving a comprehensive model for multiobjective project portfolio selection. Comput. Oper. Res. 37(4), 630–639 (2010)
Castro, M.: Development and implementation of a framework for I&D in public organizations. Master’s thesis, Universidad Autonoma de Nuevo León (2007)
Garcia R.: Hyper-Heuristicforsolving social portfolio problem. Master’s thesis, Instituto Tecnológico de Cd., Madero (2010)
Fernandez, E., Navarro, J.: A genetic search for exploiting a fuzzy preference model of portfolio problems with public projects. Ann. OR 117(191–213), 191–213 (2002)
Fernandez, E., Navarro, J.: A new approach to multicriteria sorting problems based on fuzzy outranking relations: the THESEUS method. Eur. J. Oper. Res. 213, 405–413 (2011)
Cruz-Reyes, L., Fernandez, E., Olmedo, R., Sanchez, P., Navarro, J.: Preference Incorporation into evolutionary multiobjective optimization using preference information implicit in a set of assignment examples. In: Proceedings of Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support. Atlantis Press (2013)
Rivera, G., Gomez, C., Fernandez, E., Cruz, L., Castillo, O., Bastiani, S.: Handling of synergy into an algorithm for project portfolio selection. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems, pp. 417–430. Springer, Berlin (2013)
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This work was partially financed by CONACYT, PROMEP and DGEST.
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Cruz-Reyes, L., Fernandez, E., Gomez, C., Sanchez, P. (2014). Preference Incorporation into Evolutionary Multiobjective Optimization Using a Multi-Criteria Evaluation Method. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_37
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DOI: https://doi.org/10.1007/978-3-319-05170-3_37
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