  Evolutionary multiobjective optimization has witnessed remarkable progress
during the past decades. However, existing algorithms often encounter
computational challenges in large-scale scenarios, primarily attributed to the
absence of hardware acceleration. In response, we introduce a Tensorized
Reference Vector Guided Evolutionary Algorithm (TensorRVEA) for harnessing the
advancements of GPU acceleration. In TensorRVEA, the key data structures and
operators are fully transformed into tensor forms for leveraging GPU-based
parallel computing. In numerical benchmark tests involving large-scale
populations and problem dimensions, TensorRVEA consistently demonstrates high
computational performance, achieving up to over 1000$\times$ speedups. Then, we
applied TensorRVEA to the domain of multiobjective neuroevolution for
addressing complex challenges in robotic control tasks. Furthermore, we
assessed TensorRVEA's extensibility by altering several tensorized reproduction
operators. Experimental results demonstrate promising scalability and
robustness of TensorRVEA. Source codes are available at
\url{https://github.com/EMI-Group/tensorrvea}.
