A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization

  • Authors:

  • X. Blasco;J. M. Herrero;J. Sanchis;M. Martínez

  • Affiliations:

  • Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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Abstract

New challenges in engineering design lead to multiobjective (multicriteria) problems. In this context, the Pareto front supplies a set of solutions where the designer (decision-maker) has to look for the best choice according to his preferences. Visualization techniques often play a key role in helping decision-makers, but they have important restrictions for more than two-dimensional Pareto fronts. In this work, a new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed. Level Diagrams consists of representing each objective and design parameter on separate diagrams. This new technique is based on two key points: classification of Pareto front points according to their proximity to ideal points measured with a specific norm of normalized objectives (several norms can be used); and synchronization of objective and parameter diagrams. Some of the new possibilities for analyzing Pareto fronts are shown. Additionally, in order to introduce designer preferences, Level Diagrams can be coloured, so establishing a visual representation of preferences that can help the decision-maker. Finally, an example of a robust control design is presented - a benchmark proposed at the American Control Conference. This design is set as a six-dimensional multiobjective problem.