Using the Mountain Gazelle Optimizer to Construct Consistent Pairwise Comparison Matrices Within Acceptable Consistency Ratio Thresholds

The Analytic Hierarchy Process (AHP) fundamentally relies on the pairwise comparison matrix (PCM) as a core component in structuring multi-criteria decision-making problems, with applications spanning numerous disciplines.  This paper introduces a novel methodology for generating consistent PCMs that satisfy a predefined threshold for the consistency ratio (CR).  The proposed approach leverages the Mountain Gazelle Optimizer (MGO), a recent metaheuristic algorithm inspired by the social hierarchy and herd behavior of wild mountain gazelles.  Numerical experiments demonstrate the efficacy of the MGO algorithm in constructing consistent pairwise comparison matrices across a range of sizes.  The results indicate that the proposed method outperforms conventional algorithms, notably the genetic algorithm (GA) and particle swarm optimization (PSO), particularly for large-scale matrices.