In this paper, two variants of particle swarm optimization (PSO) are used to calculate the inverse kinematics of a new 7-revolute jointed robot arm. This robot arm is required to move from a position to the desired position with a minimum error in the workspace. A scenario has been set for this purpose. According to this scenario, it is desired that the end effector of the robot arm reach a predetermined position with the minimum error. The results obtained with Random Inertia Weight and Global Local Best Inertia Weight, are compared with the standard PSO. Moreover, the path of the robot arm obtained by cubic trajectory planning is depicted with graphs. Results that computer simulated based, reveal that PSO can be efficiently used for inverse kinematics solution. However, for the inverse kinematic solution, the PSO variables are much more effective than the standard PSO.