The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making

  • Authors:

  • Lamjed Ben Said;Slim Bechikh;Khaled Ghédira

  • Affiliations:

  • Intelligent Information Engineering Laboratory, High Institute of Management of Tunis, University of Tunis, Tunis, Tunisia;Intelligent Information Engineering Laboratory, High Institute of Management of Tunis, University of Tunis, Tunis, Tunisia;Intelligent Information Engineering Laboratory, High Institute of Management of Tunis, University of Tunis, Tunis, Tunisia

  • Venue:
  • IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
  • Year:
  • 2010

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Abstract

Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in finding a representative set of Pareto optimal solutions in the past decade and beyond. Several techniques have been proposed in the specialized literature to ensure good convergence and diversity of the obtained solutions. However, in real world applications, the decision maker is not interested in the overall Pareto optimal front since the final decision is a unique solution. Recently, there has been an increased emphasis in addressing the decision-making task in searching for the most preferred alternatives. In this paper, we introduce a new variant of the Pareto dominance relation, called r-dominance, which has the ability to create a strict partial order among Pareto-equivalent solutions. This fact makes such a relation able to guide the search toward the interesting parts of the Pareto optimal region based on the decision maker's preferences expressed as a set of aspiration levels. After integrating the new dominance relation in the NSGA-II methodology, the efficacy and the usefulness of the modified procedure are assessed through two to ten-objective test problems a priori and interactively. Moreover, the proposed approach provides competitive and better results when compared to other recently proposed preference-based EMO approaches.