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Unsatisfying functions and multiobjective fuzzy satisfaction design using genetic algorithms.

T Kiyota1, Y Tsuji, E Kondo

  • 1Dept. of Mech. Syst. & Environ. Eng., Univ. of Kitakyushu, Fukuoka, Japan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study introduces a novel fuzzy satisfaction method with genetic algorithms (GA) for multiobjective problems. It efficiently finds Pareto-optimal solutions by minimizing unsatisfaction ratings, aiding decision-makers in complex design choices.

Area of Science:

  • Operations Research
  • Computational Intelligence
  • Decision Science

Background:

  • Multiobjective optimization problems often involve complex trade-offs and imprecise goals.
  • Traditional methods struggle with the inherent fuzziness in decision-maker preferences and aspiration levels.
  • Genetic algorithms (GA) offer a robust framework for exploring complex solution spaces.

Purpose of the Study:

  • To develop a new fuzzy satisfaction method for effectively addressing multiobjective problems.
  • To integrate genetic algorithms (GA) for solving these fuzzy multiobjective optimization problems.
  • To provide decision-makers (DM) with a practical tool for identifying preferred Pareto-optimal solutions.

Main Methods:

  • Introduced an unsatisfying function, analogous to membership functions, to quantify 'fuzziness'.

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  • Transformed multiobjective design problems into constraint satisfaction problems using aspiration levels.
  • Formulated the problem as a multiobjective minimization of unsatisfaction ratings, solved using GA.
  • Developed a novel strategy within GA to generate a set of Pareto-optimal solutions relevant to the DM.

    • Main Results:

    • The proposed method successfully handles fuzziness in aspiration levels and constraints.
    • A group of Pareto-optimal solutions, tailored to DM interest, can be efficiently obtained.
    • The unsatisfying function provides a clear measure of dissatisfaction for each objective.

    Conclusions:

    • The fuzzy satisfaction method combined with GA offers an effective approach for multiobjective optimization.
    • This method empowers decision-makers to interactively explore and select solutions based on their preferences.
    • The approach enhances the practical applicability of fuzzy logic and genetic algorithms in complex decision-making scenarios.