Minimizing Inconsistency in Pairwise Comparison Matrices Using Genetic Algorithm

This paper introduces a novel approach to reduce inconsistencies in pairwise comparison matrices using a genetic algorithm inspired by the process of natural selection. The method applies a distance-based inconsistency index as the fitness function within an evolutionary process. Through experiments, we show that our genetic algorithm greatly reduces inconsistencies over generations. Smaller matrices quickly become consistent in just a few generations, showing the method’s effectiveness and efficiency for different scenarios and use cases. Larger matrices, however, require more generations to reach the acceptable consistency level. The algorithm works well for different matrix sizes, adapting effectively to various challenging situations.