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Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems.

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Title: Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems.
Authors: Lei, Dongge1 (AUTHOR), Cai, Lulu1 (AUTHOR), Wu, Fei1 (AUTHOR) wufei@qzc.edu.cn
Source: Automatika: Journal for Control, Measurement, Electronics, Computing & Communications. Dec2024, Vol. 65 Issue 4, p1640-1665. 26p.
Subjects: Metaheuristic algorithms, Imperialist competitive algorithm, Machine learning, Engineering design, Test design
Abstract: Imperialist competitive algorithm (ICA) is an efficient meta-heuristic algorithm by simulating the competitive behaviour among imperialist countries. However, it still suffers from slow convergence and deficiency in exploration. To address these issues, an improved ICA is proposed by combining ICA with a quasi-opposition-based learning (QOBL) strategy, which is named QOBL-ICA. The improvements include two aspects. First, the QOBL strategy is adopted to generate a population of fitter individuals. Second, a QOBL-assisted assimilation strategy is proposed to enhance the exploration ability of ICA. As a result, the proposed QOBL-ICA has more powerful exploration ability than ICA as well as faster convergence speed. The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed. [ABSTRACT FROM AUTHOR]
Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications 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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  Data: Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems.
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  Data: <searchLink fieldCode="AR" term="%22Lei%2C+Dongge%22">Lei, Dongge</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Lulu%22">Cai, Lulu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Fei%22">Wu, Fei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wufei@qzc.edu.cn</i>
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  Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Imperialist+competitive+algorithm%22">Imperialist competitive algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+design%22">Engineering design</searchLink><br /><searchLink fieldCode="DE" term="%22Test+design%22">Test design</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Imperialist competitive algorithm (ICA) is an efficient meta-heuristic algorithm by simulating the competitive behaviour among imperialist countries. However, it still suffers from slow convergence and deficiency in exploration. To address these issues, an improved ICA is proposed by combining ICA with a quasi-opposition-based learning (QOBL) strategy, which is named QOBL-ICA. The improvements include two aspects. First, the QOBL strategy is adopted to generate a population of fitter individuals. Second, a QOBL-assisted assimilation strategy is proposed to enhance the exploration ability of ICA. As a result, the proposed QOBL-ICA has more powerful exploration ability than ICA as well as faster convergence speed. The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications 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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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/00051144.2024.2420296
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1640
    Subjects:
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Imperialist competitive algorithm
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Engineering design
        Type: general
      – SubjectFull: Test design
        Type: general
    Titles:
      – TitleFull: Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems.
        Type: main
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          Name:
            NameFull: Lei, Dongge
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          Name:
            NameFull: Cai, Lulu
      – PersonEntity:
          Name:
            NameFull: Wu, Fei
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      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
          Identifiers:
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              Value: 00051144
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              Value: 65
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              Value: 4
          Titles:
            – TitleFull: Automatika: Journal for Control, Measurement, Electronics, Computing & Communications
              Type: main
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