In this paper, we present a study on the design optimization of the 6-RUS Stewart platform using a hybrid algorithm. The geometric and kinematic models are calculated. The optimization problem is formulated after determining the design parameters and defining a set of cost functions related to the size of the workspace and to the indices of the kinematic and static performance, which are the global conditioning index (GCI) and the global stiffness index (GSI).

We started by studying the relation between the design parameters and the proposed cost functions, and then we invested the genetic algorithm to optimize each cost function separately. Moreover, we adopted a weighted cost function method to solve the Multi-Objective optimization problem. 

The convergence performance of the genetic algorithm (GA) and the particle swarm optimization (PSO) were compared, where the PSO algorithm showed better performance. Based on this, a hybrid PSO–PS method was proposed and the results are highly competitive as we obtained better general convergence performance.