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Enhanced Artemisinin Optimization Algorithm for Engineering Design Problems.

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Title: Enhanced Artemisinin Optimization Algorithm for Engineering Design Problems.
Authors: Wang, Zhaoqiang1 wzqedu@sina.com, Liu, Hao2 liuhustl@sina.cn, Tu, Liangping2 tuliangping@ustl.edu.cn
Source: Engineering Letters. Jun2026, Vol. 34 Issue 6, p2447-2470. 24p.
Subjects: Metaheuristic algorithms, Mathematical optimization, Engineering design, Lévy processes, Benchmark problems (Computer science)
Abstract: This study proposes an enhanced Artemisinin Optimization (EAO) algorithm for numerical benchmark functions and constrained engineering design problems. To address the slow convergence and stagnation of the standard AO, the EAO integrates three complementary strategies: a diversitybased parameter adaptation mechanism to balance exploration and exploitation, a Levy flight-elite guidance synergistic strategy to enhance global search and convergence, and a onedimensional gene crossover embedded in a two-stage search scheme to improve efficiency in later iterations. The proposed EAO preserves the same time complexity as the AO while achieving superior performance. Experimental results on the CEC2022 and CEC2017 benchmark suites show that EAO attains the best overall average ranking among the eight advanced metaheuristic algorithms, with an average execution time reduction of approximately 31.25% in high-dimensional cases. Furthermore, EAO was successfully applied to four classical constrained engineering design problems, where it consistently yielded better solution quality and stability than competing algorithms, demonstrating its effectiveness as a general-purpose optimization method. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Letters 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.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zhaoqiang%22">Wang, Zhaoqiang</searchLink><relatesTo>1</relatesTo><i> wzqedu@sina.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Hao%22">Liu, Hao</searchLink><relatesTo>2</relatesTo><i> liuhustl@sina.cn</i><br /><searchLink fieldCode="AR" term="%22Tu%2C+Liangping%22">Tu, Liangping</searchLink><relatesTo>2</relatesTo><i> tuliangping@ustl.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jun2026, Vol. 34 Issue 6, p2447-2470. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+design%22">Engineering design</searchLink><br /><searchLink fieldCode="DE" term="%22Lévy+processes%22">Lévy processes</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink>
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  Label: Abstract
  Group: Ab
  Data: This study proposes an enhanced Artemisinin Optimization (EAO) algorithm for numerical benchmark functions and constrained engineering design problems. To address the slow convergence and stagnation of the standard AO, the EAO integrates three complementary strategies: a diversitybased parameter adaptation mechanism to balance exploration and exploitation, a Levy flight-elite guidance synergistic strategy to enhance global search and convergence, and a onedimensional gene crossover embedded in a two-stage search scheme to improve efficiency in later iterations. The proposed EAO preserves the same time complexity as the AO while achieving superior performance. Experimental results on the CEC2022 and CEC2017 benchmark suites show that EAO attains the best overall average ranking among the eight advanced metaheuristic algorithms, with an average execution time reduction of approximately 31.25% in high-dimensional cases. Furthermore, EAO was successfully applied to four classical constrained engineering design problems, where it consistently yielded better solution quality and stability than competing algorithms, demonstrating its effectiveness as a general-purpose optimization method. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Engineering Letters 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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        PageCount: 24
        StartPage: 2447
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      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Engineering design
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      – SubjectFull: Lévy processes
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      – SubjectFull: Benchmark problems (Computer science)
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              Text: Jun2026
              Type: published
              Y: 2026
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