Algorithm Based on Particle Swarm Optimization for Handling Incomplete Pairwise Comparison Situations in Ahp

The job of the AHP expert is to input his/her perceptions into a pairwise comparison matrix; however, there are times when the expert is unable to provide his/her opinions. This can be for many reasons such as there are numerous pairwise comparisons to be completed or the expert is unsure about the values that must be included in the pairwise comparison matrix. The AHP method cannot be performed without a complete comparison matrix; therefore, the aim of this article is to provide a novel approach based on the Particle Swarm Optimization (PSO) technique to estimate the missing values in pairwise comparison matrices. According to the findings of this study, the proposed algorithm can offer a suggested value for the pairwise comparison matrix with an acceptable Consistency Ratio (CR). Furthermore, the time required to find the suggested value is quite short.