Inconsistency reduction in pairwise comparison matrices using genetic algorithms

• Proposes efficient genetic algorithms for reducing inconsistency in pairwise comparison matrices. • Introduces one algorithm for quantitative and two for qualitative multiplicative comparisons. • Employs a distance-based inconsistency index for quantitative analysis. • Adapts suitable evaluation functions for qualitative (non-numeric) comparisons. • Illustrates the effectiveness of genetic algorithms, particularly in qualitative cases. This paper discusses inconsistency reduction in qualitative and quantitative multiplicative pairwise comparison matrices by applying efficient genetic algorithms in detail. Three new algorithms are presented and discussed. One for the classical quantitative multiplicative pairwise comparisons, and two for a formal version of qualitative pairwise comparisons. For the quantitative case, a distance-based inconsistency index (Koczkodaj’s index) is used. Moreover the effects of different factors on its efficiency and the quality of results are analyzed. For the qualitative case, no numbers are used, so evaluation functions are tailored to use qualitative relations. For both quantitative and qualitative cases, the genetic algorithms perform reliably, and in the qualitative case they show strong performance compared to the existing method we evaluated