View in EDS

Improved Grey Wolf Optimization and its application in regression testing.

Saved in:
Bibliographic Details
Title: Improved Grey Wolf Optimization and its application in regression testing.
Authors: Gupta, Aishwarya1 (AUTHOR) gupta1994aishwarya@gmail.com, Mahaur, Bharat Krishan2 (AUTHOR) mahaurbk@nitj.ac.in
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Jun2026, Vol. 30 Issue 6, p4327-4346. 20p.
Subjects: Grey Wolf Optimizer algorithm, Regression testing (Computer science), Swarm intelligence, Optimization algorithms, Subset selection, Combinatorial optimization
Abstract: Grey Wolf Optimizer (GWO) is a prominent swarm intelligence algorithm that emulates the hunting behavior of grey wolves to solve optimization problems. While GWO performs well in various scenarios, its solution generation primarily relies on exploration, lacking effective exploitation around previously discovered optimal regions. In this work, we propose an improved version of GWO that incorporates best solutions directly into the generation of new solutions, enhancing both convergence speed and solution diversity. Additionally, we present a binary variant of the improved GWO, making it suitable for binary optimization problem, such as regression testing. The proposed binary GWO is applied to the test subset selection problem in regression testing, implemented on the Siemens test suite. Comparative analysis against other swarm intelligence algorithms on benchmark functions and regression testing demonstrates that our proposed variants outperform previous approaches in terms of speed and accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature 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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 195076862
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Improved Grey Wolf Optimization and its application in regression testing.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gupta%2C+Aishwarya%22">Gupta, Aishwarya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gupta1994aishwarya@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mahaur%2C+Bharat+Krishan%22">Mahaur, Bharat Krishan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mahaurbk@nitj.ac.in</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Soft+Computing+-+A+Fusion+of+Foundations%2C+Methodologies+%26+Applications%22">Soft Computing - A Fusion of Foundations, Methodologies & Applications</searchLink>. Jun2026, Vol. 30 Issue 6, p4327-4346. 20p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+testing+%28Computer+science%29%22">Regression testing (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Subset+selection%22">Subset selection</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Grey Wolf Optimizer (GWO) is a prominent swarm intelligence algorithm that emulates the hunting behavior of grey wolves to solve optimization problems. While GWO performs well in various scenarios, its solution generation primarily relies on exploration, lacking effective exploitation around previously discovered optimal regions. In this work, we propose an improved version of GWO that incorporates best solutions directly into the generation of new solutions, enhancing both convergence speed and solution diversity. Additionally, we present a binary variant of the improved GWO, making it suitable for binary optimization problem, such as regression testing. The proposed binary GWO is applied to the test subset selection problem in regression testing, implemented on the Siemens test suite. Comparative analysis against other swarm intelligence algorithms on benchmark functions and regression testing demonstrates that our proposed variants outperform previous approaches in terms of speed and accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195076862
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00500-025-11034-8
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 4327
    Subjects:
      – SubjectFull: Grey Wolf Optimizer algorithm
        Type: general
      – SubjectFull: Regression testing (Computer science)
        Type: general
      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Subset selection
        Type: general
      – SubjectFull: Combinatorial optimization
        Type: general
    Titles:
      – TitleFull: Improved Grey Wolf Optimization and its application in regression testing.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Gupta, Aishwarya
      – PersonEntity:
          Name:
            NameFull: Mahaur, Bharat Krishan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 14327643
          Numbering:
            – Type: volume
              Value: 30
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Soft Computing - A Fusion of Foundations, Methodologies & Applications
              Type: main
ResultId 1