Numerous Algorithms have recently been invented with varying strengths and weaknesses, none of which is the best for all cases. Herein, a hybrid Optimization method known as a PSOHHO Optimization Algorithm is presented. There are two methods for combining Algorithms: parallel and sequential. We adopted the parallel method and optimized the Algorithm's performance. We cover the weaknesses of one Algorithm with the strengths of another Algorithm using a new method of combination. In this method, using several formulas, the top populations are exchanged between the two Algorithms, and a new population is created. With this ability, the strengths of an Algorithm can be used to compensate for the weaknesses of the other Algorithm. In this method, no changes are made to the Algorithms. The main goal is to use existing Algorithms. This method aims to attain the optimal solution in the shortest time possible. Two Algorithms of particle swarm Optimization (PSO) and Harris Hawks Optimization (HHO) were used to present this method and five truss samples were considered to confirm the performance of this method. Based on the results, this method has rapid convergence speed and acceptable results compared to the other methods. It also yields better results than its basic Algorithms.