The paper proposes a novel hybrid optimization algorithm, PO-PSO which includes Metaheuristic adaptation algorithms play an important role in solving complicated problems with the real world. In this study, a new and improved version of Partot Optimizer (PO) by hybridization to improve convergence speed, solution accuracy and general performance. The parrot optimizer with PSO integrates POS exploration skills with the utilization efficiency of the Optimizer PSO, leading to a more balanced exploration system. The proposed algorithm is evaluated on benchmark -adaptation functions and real world problems, which perform better performance than standard pos, psos and other traditional adaptation methods. Experimental results suggest that HPOPSO gets rapid convergence and high solution quality, making it a promising approach to complex adaptation functions. This article is a new adaptation method inspired by the specific behavior of the trained pyirhura Molina, which presents Paper Parrot Optimizer (PO)[3].