SUMMARY
The paper discussed the process to find the optimum dimension for the kinematic constants of a two-degree of freedom planar parallel manipulator. This manipulator itself was constructed by symmetric three parallelogram chains. An optimization process using non-sorted dominated genetic algorithm II (NSGA-II) was carried out for maximization of (i) rMIC (the radius of the maximum inscribed circle) and GCI (global conditioning index), and (ii) rMIC and GTI (global transmission index). Here, GCI and GTI were evaluated on the useful workspace. Instead of using atlases of performance indices, a grid search evaluation was applied to obtain a region in PDS near the optimum values for both maximization cases. This region gave a small bound for NSGA-II to start searching the optimum values of the kinematic constants. For simplification, a python framework for the multi-objective optimization called pymoo was applied to solve the optimization problem. Henceforth, the maximization for two cases yielded an insignificant difference of results in terms of optimum kinematic constants, rMIC, GCI, GTI, area of useful workspace, area of good condition workspace (GCW), area of good transmission workspace (GTW), and the area ratio of GCW and GTW to the useful workspace.