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面向大规模系统的并行进化策略框架.

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Title: 面向大规模系统的并行进化策略框架.
Alternate Title: A parallel evolution strategy framework for large-scale system.
Authors: 张涵1, 王小平1 xpwang@hnu.edu.cn
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Jan2026, Vol. 48 Issue 1, p11-19. 9p.
Subjects: Large scale systems, Fault tolerance (Engineering), Optimization algorithms, Mathematical optimization, Parallel programming, Evolutionary computation, Reinforcement learning, Parallel processing
Abstract (English): Evolution strategies (ES) algorithm is an efficient optimization algorithm suitable for solving problems where gradient information is either unavailable or difficult to obtain. Tt is widely applied in tasks such as reinforcement learning and black-box optimization. As the scale and complexity of problems increase, the sampling size of the ES algorithm also grows larger, leading to a corresponding increase in computational parallelism. For large-scale systems, a new parallel ES algorithm framework is proposed, primarily focusing on optimizing fault-tolerant computing and communication overhead during ultra-large-scale parallel execution of the algorithm. To address these issues, a high-concurrency reduction mechanism is introduced, along with a low-overhead fault-tolerance method tailored to the algorithm's characteristics. Experimental results demonstrate that the parallel efficiency of the new algorithm framework in large-scale systems exceeds 54. 7%, and when the parallel scale expands to tens of thousands of nodes, the parallel efficiency is 23% higher than OpenAT-NES. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 进化策略算法是一种高效的优化算法, 适用于解决无梯度信息或难以获取梯度信息的问题, 广泛应用于强化学习和黑盒优化等任务。随着问题规模和复杂度的增加, 进化策略算法的采样规模也越 来越大, 相应地, 计算并行度也随之增加。面向大规模系统, 提出了新的并行进化策略算法框架, 主要优化 进化策略算法在超大规模并行执行中的容错计算和通信开销问题。针对这些问题, 提出了高并发的规约 机制, 并针对算法特点提出了低开销的容错方法。实验显示, 新的并行进化策略算法框架在大规模系统中 的并行效率在54. 7%以上, 且在并行规模上升至上万节点时, 并行效率比OpenAT-NES高出23%. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: 面向大规模系统的并行进化策略框架.
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  Data: A parallel evolution strategy framework for large-scale system.
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  Data: <searchLink fieldCode="AR" term="%22张涵%22">张涵</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22王小平%22">王小平</searchLink><relatesTo>1</relatesTo><i> xpwang@hnu.edu.cn</i>
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  Data: <searchLink fieldCode="DE" term="%22Large+scale+systems%22">Large scale systems</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+tolerance+%28Engineering%29%22">Fault tolerance (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+computation%22">Evolutionary computation</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: Evolution strategies (ES) algorithm is an efficient optimization algorithm suitable for solving problems where gradient information is either unavailable or difficult to obtain. Tt is widely applied in tasks such as reinforcement learning and black-box optimization. As the scale and complexity of problems increase, the sampling size of the ES algorithm also grows larger, leading to a corresponding increase in computational parallelism. For large-scale systems, a new parallel ES algorithm framework is proposed, primarily focusing on optimizing fault-tolerant computing and communication overhead during ultra-large-scale parallel execution of the algorithm. To address these issues, a high-concurrency reduction mechanism is introduced, along with a low-overhead fault-tolerance method tailored to the algorithm's characteristics. Experimental results demonstrate that the parallel efficiency of the new algorithm framework in large-scale systems exceeds 54. 7%, and when the parallel scale expands to tens of thousands of nodes, the parallel efficiency is 23% higher than OpenAT-NES. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 进化策略算法是一种高效的优化算法, 适用于解决无梯度信息或难以获取梯度信息的问题, 广泛应用于强化学习和黑盒优化等任务。随着问题规模和复杂度的增加, 进化策略算法的采样规模也越 来越大, 相应地, 计算并行度也随之增加。面向大规模系统, 提出了新的并行进化策略算法框架, 主要优化 进化策略算法在超大规模并行执行中的容错计算和通信开销问题。针对这些问题, 提出了高并发的规约 机制, 并针对算法特点提出了低开销的容错方法。实验显示, 新的并行进化策略算法框架在大规模系统中 的并行效率在54. 7%以上, 且在并行规模上升至上万节点时, 并行效率比OpenAT-NES高出23%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3969/j.issn.1007-130X.2026.01.002
    Languages:
      – Code: chi
        Text: Chinese
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      Pagination:
        PageCount: 9
        StartPage: 11
    Subjects:
      – SubjectFull: Large scale systems
        Type: general
      – SubjectFull: Fault tolerance (Engineering)
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Parallel programming
        Type: general
      – SubjectFull: Evolutionary computation
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Parallel processing
        Type: general
    Titles:
      – TitleFull: 面向大规模系统的并行进化策略框架.
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            NameFull: 张涵
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            NameFull: 王小平
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            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
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            – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue
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