In order to solve the problems of slow convergence speed

poor convergence accuracy and insufficient stability in applying the traditional mayfly optimization algorithm to a robot′s path planning

an improved mayfly optimization algorithm is proposed. In the improved mayfly optimization algorithm

a dynamic parameter adjustment strategy is introduced to achieve a better balance between local search and global search. The Levy flight strategy and the exiting strategy are combined to avoid falling into local optimization. The improved mayfly optimization algorithm is randomly tested for 20 times in a 20×20 grid model. The simulation results show that the improved optimization algorithm significantly improves the solving accuracy and speed and the smoothness of the path of the robot. At the same time

in order to further verify the reliability and effectiveness of the improved mayfly optimization algorithm

it is randomly tested for 20 times with the 30×30 grid model. The results show that the reliability and stability of the improved optimization algorithm are improved.