Reconstructing pairwise comparisons matrices based on differential evolution: a Monte Carlo study

Abstract

Pairwise comparisons have been used in the decision-making process since antiquities. However, it is a substantial challenge to generate a PC matrix from noisy or incomplete real-life input data. This study aims to investigate the reconstruction of pairwise comparisons matrices from not-so-inconsistent pairwise comparisons matrices by an optimization method based on di↵erential evolution. A distance-based objective function is defined as a function of the inconsistency indicator and the distance metric. Monte Carlo experiments are designed to illustrate the research outcomes. The experimental results show that this method convergence quickly. It also provides comparisons of several traditional metrics.