POO-LPSP: Parallel Osprey Optimized Least Penalty-Squared Prioritization Methods for Priority Derivation in the Analytic Hierarchy Process

This paper proposes POO-LPSP, a parallel Osprey Optimization Algorithm for solving revised Least Penalty-Squared Prioritization models in the Analytic Hierarchy Process. The framework includes revised Least Product of Penalty and Direct Squares (LPPDS) and revised Weighted Squares (LPPWS), targeting minimization of RMPSV and RMPSWV. A Generative AI vendor-selection example is used to demonstrate practical utility and computational efficiency. The abstract presents POO-LPSP as an alternative to Saaty’s eigenvector method, but does not report dataset size, baseline comparisons, runtime figures, statistical tests, or reproducibility details.