Abstract
The resolution of a multi-objective optimization problem involves not just a search and computation phase, capable of providing a representative sample of the Pareto-optimal front, but also a decision support process to aid the Decision Maker (DM) to progress in the learning of the trade-offs at stake in different regions of the search space. This is accomplished by integrating in the search process the DM’s preferences to guide the search and limit both the cognitive effort, in assessing Pareto-optimal solutions with distinct characteristics, and the computational effort, by reducing the scope of the search according to the preferences expressed by the DM. The introduction of meaningful preference expression parameters used in the ELECTRE TRI method for sorting problems in the framework of an evolutionary algorithm is proposed. Illustrative results in an operational planning problem in electricity networks are reported.
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References
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
Coello, C.A.C., Veldhuizen, D.V., Lamont, G.B.: Evolutionary Algorithms for Solving Multiobjective Problems. Kluwer Academic Publishers, New York (2002)
Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective evolutionary algorithms: analyzing the state-of the-art. Evol. Comput. 8(2), 125–148 (2000)
Horn, J.: Multicriterion decision making. In: Bäck, T., Fogel, D., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, vol. 1, pp. F1.9:1–F1.9:15.5. Oxford Univ. Press, Oxford (1997)
Coello, C.A.C.: Handling Preferences in Evolutionary Multiobjective Optimization: A Survey. In: Proc. of the 2000 Congress on Evo. Computation, pp. 30–37 (2000)
Roy, B.: Multicriteria Methodology for Decision Aiding. Springer, Dordrecht (1996)
Roy, B.: The outranking approach and the foundation of ELECTRE methods. Theory and Decision 31, 49–73 (1991)
Mousseau, V., Slowinski, R., Zielniewicz, P.: A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support. Comp. and Operations Research 27, 757–777 (2000)
Zopounidis, C., Doumpos, M.: Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research 138, 229–246 (2002)
Zhang, W., Li, F., Tolbert, L.M.: Review of Reactive Power Planning: Objectives, Constraints, and Algorithms. IEEE Transctions on Power Systems 22, 2177–2186 (2007)
Antunes, C.H., Barrico, C., Gomes, A., Pires, D.F., Martins, A.G.: An evolutionary algorithm for reactive power compensation in radial distribution networks. Applied Energy 86, 977–984 (2009)
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Oliveira, E., Henggeler Antunes, C. (2010). An Evolutionary Algorithm Guided by Preferences Elicited According to the ELECTRE TRI Method Principles. In: Cowling, P., Merz, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2010. Lecture Notes in Computer Science, vol 6022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12139-5_19
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DOI: https://doi.org/10.1007/978-3-642-12139-5_19
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