In many real-life design situations, there are several different criteria that we want to optimize, and these criteria are often in conflict with each other. Traditionally, such multi-criteria optimization situations are handled in an ad hoc manner, when different conflicting criteria are artificially combined into a single combination objective that is then optimized. The use of unnatural ad hoc tools is clearly not the best way of describing a very natural aspect of human reasoning. Fuzzy logic describes a much more natural way of handling multi-criterion optimization problems: when we cannot maximize each of the original conflicting criteria 100%, we optimize each to a certain extent.
Several methods have been proposed in fuzzy logic to handle multi-criteria optimization. These methods, however, still use some ad hoc ideas. In this paper, we show that some approaches to multi-objective optimization can be justified based on the fuzzy logic only and do not require any extra ad hoc tools.