Fuzzy Multi-Objective Optimization

  • Freerk A. Lootsma
Part of the Applied Optimization book series (APOP, volume 8)

Abstract

Many features of real-life single-objective optimization problems are imprecise. The values of the coefficients are sometimes merely prototypical, the requirement that the constraints must be satisfied may be somewhat relaxed, and the decision makers are not always very satisfied with the value attained by the objective function. Multi-Objective Optimization introduces a new feature: the degrees of satisfaction with the objective-function values play a major role because they enable the decision makers to control the convergence towards an acceptable compromise solution. Since the objective functions have different weights for the decision maker we also have to control the computational process via weighted degrees of satisfaction.

Keywords

Objective Function Fuzzy Logic Indifference Curve Nondominated Solution Weighted Degree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Freerk A. Lootsma
    • 1
  1. 1.Delft University of TechnologyThe Netherlands

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