Objective To address the limitations of existing optimization algorithms in distribution network fault location specifically slow convergence speed during later iterations and low positioning accuracy an improved gray wolf optimizer IGWO is proposed. Methods First a switching function and a fitness function suitable for distributed generation DG integration were constructed. An IEEE 33-node distribution network model was then established based on these functions. Subsequently the gray wolf optimizer was discretized to accommodate distribution networks with DG. Second inspired by the somersault-foraging mechanism in the manta ray foraging optimizer a wavelet-function-based perturbation strategy was incorporated into the optimal solution to enhance the ability to escape local optima and accelerate convergence. Additionally a nonlinear processing scheme was applied to the convergence factor to improve local search capability computational speed and solution accuracy. Results Simulation tests for single-point faults multi-point faults and information distortion were conducted by connecting DGs at varying locations and quantities. Comparative analyses against the binary gray wolf optimizer original gray wolf optimizer and particle swarm optimizer demonstrated that the IGWO achieved superior performance in positioning accuracy average convergence generation and iteration time. Conclusion The IGWO outperforms the other three optimization algorithms in terms of convergence speed and positioning efficiency in fault location.