In to achieve efficient search capability for path planning of mobile robots in complex environments, a hybrid multi-strategy improved dung beetle optimizer has been proposed. Firstly, the ISPM chaos strategy is introduced to initialize the initial population of fireflies. This ensures a more uniform distribution of the initial population and reduces the likelihood of the algorithm getting stuck in local optima. Then, the greedy selection strategy is combined with the improved lens imaging reverse learning strategy to update the positions of the fireflies during their foraging behavior. This balances the algorithm′s local exploration and global search capa-bilities, thereby enhancing its convergence ability. Finally, the Levy flight strategy and an improved dynamic weight update mechanism are employed to update the positions of the fireflies during their stealing behavior. This helps to change the optimal global solution and prevent the algorithm from getting trapped in local optima. To evaluate the performance of the improved algorithm, comparative experiments are conducted with four other swarm intelligence algorithms using benchmark test functions and simulation of path optimization. The experimental results demonstrate that the improved dung beetle optimizer significantly improves convergence speed and optimization accuracy, while maintaining good robustness.