Aiming at the problems that the whale optimization algorithm is easy to fall into local optimum and low convergence accuracy when optimizing complex engineering, a whale optimization algorithm (ELWOA) based on elite backward learning and Lévy flight is proposed, which first optimizes the initialized population through elite backward learning to improve the diversity of the population; then increases the adaptive weight factor, which is beneficial to balance the global and local search ability of the algorithm; finally, the Lévy flight strategy is applied to the whale optimization algorithm to conduct a small search near the optimal position, which is beneficial to the algorithm to jump out of the local optimum later and improve the local search ability of the algorithm. Through the simulation and optimization analysis of several test functions, the results show that the ELWOA algorithm has faster convergence speed and better convergence accuracy than the WOA and MWOA algorithms.