an improved qpso algorithm based on robust multi-objective optimization

In the design and optimization of engineering systems, even a small change in the variables can lead to large changes in the objective functions. therefore, the study of methods capable of producing solutions with less sensitivity to disturbances in variables is of special importance. this article presents a new methodology for solving robust multi-objective optimization problems using the quantum-behaved particle swarm optimization (qpso), and the algorithm s effectiveness has been validated through experimental results on an industrial robot. the evaluation results demonstrate that the utilization of qpso effectively enhances the performance and efficiency of the industrial robot. also, the simulation results indicate that this design method is an interesting approach for obtaining robust designs in many engineering applications.