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A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems

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  • Zio, E.
  • Bazzo, R.

Abstract

In many multiobjective optimization problems, the Pareto Fronts and Sets contain a large number of solutions and this makes it difficult for the decision maker to identify the preferred ones. A possible way to alleviate this difficulty is to present to the decision maker a subset of a small number of solutions representatives of the Pareto Front characteristics. In this paper, a two-steps procedure is presented, aimed at identifying a limited number of representative solutions to be presented to the decision maker. Pareto Front solutions are first clustered into "families", which are then synthetically represented by a "head-of-the-family" solution. Level Diagrams are then used to represent, analyse and interpret the Pareto Front reduced to its head-of-the-family solutions. The procedure is applied to a reliability allocation case study of literature, in decision-making contexts both without or with explicit preferences by the decision maker on the objectives to be optimized.

Suggested Citation

  • Zio, E. & Bazzo, R., 2011. "A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 624-634, May.
  • Handle: RePEc:eee:ejores:v:210:y:2011:i:3:p:624-634
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    1. Roy, B. & Bouyssou, D., 1986. "Comparison of two decision-aid models applied to a nuclear power plant siting example," European Journal of Operational Research, Elsevier, vol. 25(2), pages 200-215, May.
    2. Molina, Julin & Santana, Luis V. & Hernandez-Daz, Alfredo G. & Coello Coello, Carlos A. & Caballero, Rafael, 2009. "g-dominance: Reference point based dominance for multiobjective metaheuristics," European Journal of Operational Research, Elsevier, vol. 197(2), pages 685-692, September.
    3. Zio, E. & Baraldi, P. & Pedroni, N., 2009. "Optimal power system generation scheduling by multi-objective genetic algorithms with preferences," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 432-444.
    4. Katagiri, Hideki & Sakawa, Masatoshi & Kato, Kosuke & Nishizaki, Ichiro, 2008. "Interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability," European Journal of Operational Research, Elsevier, vol. 188(2), pages 530-539, July.
    5. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, August.
    6. Yang, Jian-Bo, 2000. "Minimax reference point approach and its application for multiobjective optimisation," European Journal of Operational Research, Elsevier, vol. 126(3), pages 541-556, November.
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