Evolutionary multiobjective optimization (EMO) has made significant strides
over the past two decades. However, as problem scales and complexities
increase, traditional EMO algorithms face substantial performance limitations
due to insufficient parallelism and scalability. While most work has focused on
algorithm design to address these challenges, little attention has been given
to hardware acceleration, thereby leaving a clear gap between EMO algorithms
and advanced computing devices, such as GPUs. To bridge the gap, we propose to
parallelize EMO algorithms on GPUs via the tensorization methodology. By
employing tensorization, the data structures and operations of EMO algorithms
are transformed into concise tensor representations, which seamlessly enables
automatic utilization of GPU computing. We demonstrate the effectiveness of our
approach by applying it to three representative EMO algorithms: NSGA-III,
MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a
multiobjective robot control benchmark using a GPU-accelerated physics engine.
Our experiments show that the tensorized EMO algorithms achieve speedups of up
to 1113x compared to their CPU-based counterparts, while maintaining solution
quality and effectively scaling population sizes to hundreds of thousands.
Furthermore, the tensorized EMO algorithms efficiently tackle complex
multiobjective robot control tasks, producing high-quality solutions with
diverse behaviors. Source codes are available at
https://github.com/EMI-Group/evomo.