Preference Incorporation into Evolutionary Multiobjective Optimization Using a Multi-Criteria Evaluation Method
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.
KeywordsPareto Front Pareto Optimal Solution Pareto Frontier Multiobjective Optimization Problem Multiobjective Evolutionary Algorithm
This work was partially financed by CONACYT, PROMEP and DGEST.
- 2.Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving MultiObjective Problems, 2nd edn. Springer, New York (2007)Google Scholar
- 4.Jaimes, A.L., Martinez, S.Z., Coello, C.A.: An introduction to multiobjective optimization techniques. Optim. Polym. Process. 29–57 (2009)Google Scholar
- 6.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)Google Scholar
- 8.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)Google Scholar
- 9.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)
- 11.Castro, M.: Development and implementation of a framework for I&D in public organizations. Master’s thesis, Universidad Autonoma de Nuevo León (2007)Google Scholar
- 12.Garcia R.: Hyper-Heuristicforsolving social portfolio problem. Master’s thesis, Instituto Tecnológico de Cd., Madero (2010)Google Scholar
- 15.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)Google Scholar
- 16.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)CrossRefGoogle Scholar