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Improved Snow Geese Algorithm with a Probability-Based Convergence Factor for Solving Economic Load Dispatch Problems.

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Title: Improved Snow Geese Algorithm with a Probability-Based Convergence Factor for Solving Economic Load Dispatch Problems.
Authors: Xiong, Zhitong1 3316662457@qq.com
Source: IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2305-2315. 11p.
Subjects: Load dispatching in electric power systems, Swarm intelligence, Nonconvex programming, Optimization algorithms, Metaheuristic algorithms
Abstract: To enhance the convergence efficiency and solution accuracy of swarm intelligence algorithms for complex nonconvex optimization problems, this paper proposes an improved Snow Geese Algorithm with a Probability Based Convergence Factor, termed CF-ISGA. The introduced convergence factor enables adaptive step size adjustment, effectively balancing global exploration and local exploitation. The effectiveness of CF-ISGA is validated on the CEC2022 benchmark functions and a 40 unit Economic Load Dispatch (ELD) problem, with comparisons against several representative metaheuristic algorithms. Experimental results demonstrate that CF-ISGA achieves faster convergence, higher solution accuracy, and better robustness. In particular, CF-ISGA obtains the minimum generation cost in the 40 unit ELD system, confirming its strong global optimization capability. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: Improved Snow Geese Algorithm with a Probability-Based Convergence Factor for Solving Economic Load Dispatch Problems.
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  Data: <searchLink fieldCode="AR" term="%22Xiong%2C+Zhitong%22">Xiong, Zhitong</searchLink><relatesTo>1</relatesTo><i> 3316662457@qq.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jun2026, Vol. 53 Issue 6, p2305-2315. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Load+dispatching+in+electric+power+systems%22">Load dispatching in electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Nonconvex+programming%22">Nonconvex programming</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To enhance the convergence efficiency and solution accuracy of swarm intelligence algorithms for complex nonconvex optimization problems, this paper proposes an improved Snow Geese Algorithm with a Probability Based Convergence Factor, termed CF-ISGA. The introduced convergence factor enables adaptive step size adjustment, effectively balancing global exploration and local exploitation. The effectiveness of CF-ISGA is validated on the CEC2022 benchmark functions and a 40 unit Economic Load Dispatch (ELD) problem, with comparisons against several representative metaheuristic algorithms. Experimental results demonstrate that CF-ISGA achieves faster convergence, higher solution accuracy, and better robustness. In particular, CF-ISGA obtains the minimum generation cost in the 40 unit ELD system, confirming its strong global optimization capability. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 2305
    Subjects:
      – SubjectFull: Load dispatching in electric power systems
        Type: general
      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Nonconvex programming
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
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      – TitleFull: Improved Snow Geese Algorithm with a Probability-Based Convergence Factor for Solving Economic Load Dispatch Problems.
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            – D: 01
              M: 06
              Text: Jun2026
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              Y: 2026
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