The quantum approximate optimization algorithm (QAOA) has numerous promising applications on solving the combinatorial optimization problems on the near-term Noisy Intermediate Scalable Quantum (NISQ) devices. QAOA has a quantum-classical hybrid structure, with the quantum part consisting the parameterized alternating operator ansatz, and the classical part consist of an optimization algorithm optimizing the parameters to maximize the expectation value. This value depends highly on the parameters. This implies that a set of good parameters leads to an accurate solution of the given problem. However, at large circuit depth, it is difficult to achieve global optimization due to the multiple occurrence of local minima. Therefore, we study the so-called leapfrogging strategy on solving the Max-cut problem for 3-regular graphs, which reuses the optimized parameters in larger graphs.