Aiming at the shortcomings of the reptile search algorithm, such as slow convergence speed, low optimization accuracy and easy to fall into local extremum, a reptile search algorithm based on water wave evolution and dynamic Levy flight is proposed. The Halton sequence is used to initialize the population to generate uniformly distributed individual, reducing the individual search blind spots and overlapping probability to improve population diversity. The water wave dynamic evolution factor and adaptive weight are introduced to coordinate the conversion between global search and local development of the algorithm, and improve algorithm convergence speed and optimization accuracy. Combining a dynamic Levy flight mutation strategy, the local anti-stagnation ability of the algorithm is improved. Through the comparative analysis of optimization of 14 benchmark test functions, the Wilcoxon rank sum test and the comparison results of the search time show that the improved algorithm has better convergence performance and optimization. performance and robustness. Finally, through the optimization comparison results of welded beam design in engineering applications, the superiority of the improved algorithm to deal with practical engineering problems is further verified.