title: Bridging Evolutionary Multiobjective Optimization and GPU Acceleration
    via Tensorization
summary: 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.
paper_authors._id: 67eaa88c40bebc3127ade04c
paper_authors.name: Zhenyu Liang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-31T15:15:16.646Z
paper_authors._id: 67e77099284080c98d8c9bfc
paper_authors.avatarUrl: /avatars/a3120e8d9b1312d8a670161b674f3196.svg
paper_authors.fullname: Zhenyu Liang
paper_authors.type: user
paper_authors.user: ZhenyuLiang
paper_authors._id: 67eaa88c40bebc3127ade04d
paper_authors.name: Hao Li
paper_authors._id: 67eaa88c40bebc3127ade04e
paper_authors.name: Naiwei Yu
paper_authors._id: 67eaa88c40bebc3127ade04f
paper_authors.name: Kebin Sun
paper_authors._id: 67eaa88c40bebc3127ade050
paper_authors.name: Ran Cheng
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-11T06:18:27.977Z
paper_authors._id: 67f8b4243f7d7fb86a2e5747
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Xao9Hs3oSYJD7qPrzdIiE.png
paper_authors.fullname: Ran Cheng
paper_authors.type: user
paper_authors.user: PeterCheng333
paper_title: Bridging Evolutionary Multiobjective Optimization and GPU Acceleration
    via Tensorization
paper_summary: 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.
paper_ai_summary: Tensorization methodology accelerates evolutionary multiobjective optimization (EMO) algorithms on GPUs, achieving substantial speedups and scalability while maintaining solution quality.
paper_ai_keywords: evolutionary multiobjective optimization EMO tensorization GPUs NSGA-III MOEA/D HypE multiobjective robot control population sizes