Fuzzy Exponents for Heuristic Based Applications

Abstract

The fuzzy extensions for heuristic based optimizing algorithrns often face the problem of increased number of calculations required to fmd the solutions. Even in linear cases the rwming time can be significantly larger than for crisp versions. In several management decision making processes the uncertainty of circumstances are represented by fuzzy sets and numbers, and non-linear features occur as well. In order to handie that kind of non-linearity, appropriate representation of the fuzzy power function is to be used that can keep the required c01nputation time and resources at a reasonable level. In the paper different solutions are compared from practical points of views when the bacterial evolutionary algorithm is used for the approximation of the optimum. Suggestions for the representation of fuzzy exponents are made as weiL