Political Optimizer is a metaheuristic algorithm proposed by scholars recently. It reflects good convergence speed as well as exploitation and exploration capabilities in problems relevant to finding optimal solutions. However, regarding the global optimum, it still has spaces to be improved. This study proposed a Hybrid Political Algorithm, which makes balanced modifications to the exploration process and algorithm functions to effectively improve searching for the optimal solutions in the mathematic problems of Congress on Evolutionary Computation and engineering problems by enabling the particles to move smarter in the computation process. Based on the experimental results, improving the performance of algorithm functions using the methods proposed in this study can also help avoid falling into the local optimum. In addition, compared to other algorithms, this improved algorithm can generate more accurate results.