Inverse kinematics is the basis of motion control, trajectory planning and dynamics analysis of redundant robots, and it is also one of the most important problems in robotics. Taking the minimum error of the position and pose of the end effector as the optimization objective, the fitness function was established, and the inverse kinematics problem of redundant manipulator was transformed into an equivalent optimization problem. Based on the swarm intelligence optimization algorithm, the hybrid mutation Drosophila optimization algorithm (HMFOA) was applied to solve the inverse kinematics problem of redundant manipulator. Using olfactory search hybrid mutation mechanism and visual search dynamic real-time update mechanism could effectively solve the convergence problem of fruit fly optimization algorithm (FOA) and improve the convergence speed of the algorithm. In order to further verify the effectiveness of HMFOA, HMFOA was tested on a 7-DOF manipulator, and the results were compared with FOA, LGMS-FOA and AE-LGMS-FOA. The experimental results show that HMFOA can effectively solve the inverse kinematics problem of redundant manipulators.