Fuzzy Multi-Objective Optimization
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.
KeywordsObjective Function Fuzzy Logic Indifference Curve Nondominated Solution Weighted Degree
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