ABSTRACT The traditional analytic hierarchy process (AHP) has significant limitations in evaluating cloud‐edge clock synchronization strategies, particularly when dealing with inconsistent judgment matrices and subjective factors. To address this methodological challenge, we propose the fuzzy modified particle swarm optimization AHP (FuzzyMPsoAhp) method, which combines particle swarm optimization (PSO) particle encoding for efficient search space compression, a dual‐phase sensitivity adaptive fitness function (DSAFF) to balance “finding optimal consistency” and “respecting expert opinions”, a two‐layer adaptive heterogeneous fuzzy logic for enhanced global exploration and local exploitation capabilities, and a heat‐map based Scout module to transform blind random search into intelligent guided exploration. To rigorously validate the optimization performance of the proposed method, we conduct extensive experiments using benchmark mathematical functions and simulated judgment matrices. The results demonstrate that the proposed method reduces the average consistency ratio by 43.3% and 26.1% compared to GaAhp and PsoAhp, respectively. This study primarily contributes a robust and efficient optimization tool for improving the consistency of AHP‐based decision matrices, paving the way for its adoption in other complex system evaluation domains.
Weiran et al. (Sun,) studied this question.