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A novel batch-selection strategy for parallel global optimization.

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Názov: A novel batch-selection strategy for parallel global optimization.
Autori: Liu, Jiawei, Xiao, Yike, Duan, Xiaojun, Chen, Xuan, Wang, Zhengming, Liu, Zecong
Zdroj: Engineering Optimization; Mar2025, Vol. 57 Issue 3, p599-623, 25p
Predmety: GLOBAL optimization, COMPUTER simulation, ALGORITHMS, ENGINEERING
Abstrakt: Bayesian-based parallel global optimization methods have become increasingly popular for solving black-box functions using parallel resources. When the function exhibits multimodality, it is difficult to identify the global optimum. This article proposes a novel batch selection strategy for parallel global optimization called Sampling–Optimizing–Selecting (SOS) to address this problem. SOS, utilizing minimum energy design, gradient optimization and hypersphere clustering, can adaptively explore multiple local optimal regions of the objective function simultaneously and make full use of parallel resources to obtain the global optimum efficiently. Additionally, the SOS algorithm maintains the sparsity between sample points to a certain extent, which enhances the accuracy of the surrogate model and thus improves the robustness of the optimization effect. Several numerical simulations and optimization experiments involving array decoy jamming are presented to demonstrate the superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Optimization is the property of Taylor & Francis Ltd 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.)
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  Label: Title
  Group: Ti
  Data: A novel batch-selection strategy for parallel global optimization.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Jiawei%22">Liu, Jiawei</searchLink><br /><searchLink fieldCode="AR" term="%22Xiao%2C+Yike%22">Xiao, Yike</searchLink><br /><searchLink fieldCode="AR" term="%22Duan%2C+Xiaojun%22">Duan, Xiaojun</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xuan%22">Chen, Xuan</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhengming%22">Wang, Zhengming</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zecong%22">Liu, Zecong</searchLink>
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  Data: Engineering Optimization; Mar2025, Vol. 57 Issue 3, p599-623, 25p
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  Data: <searchLink fieldCode="DE" term="%22GLOBAL+optimization%22">GLOBAL optimization</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+simulation%22">COMPUTER simulation</searchLink><br /><searchLink fieldCode="DE" term="%22ALGORITHMS%22">ALGORITHMS</searchLink><br /><searchLink fieldCode="DE" term="%22ENGINEERING%22">ENGINEERING</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Bayesian-based parallel global optimization methods have become increasingly popular for solving black-box functions using parallel resources. When the function exhibits multimodality, it is difficult to identify the global optimum. This article proposes a novel batch selection strategy for parallel global optimization called Sampling–Optimizing–Selecting (SOS) to address this problem. SOS, utilizing minimum energy design, gradient optimization and hypersphere clustering, can adaptively explore multiple local optimal regions of the objective function simultaneously and make full use of parallel resources to obtain the global optimum efficiently. Additionally, the SOS algorithm maintains the sparsity between sample points to a certain extent, which enhances the accuracy of the surrogate model and thus improves the robustness of the optimization effect. Several numerical simulations and optimization experiments involving array decoy jamming are presented to demonstrate the superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Optimization is the property of Taylor & Francis Ltd 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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        Value: 10.1080/0305215X.2024.2328788
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        Text: English
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              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
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