Enhanced Artemisinin Optimization Algorithm for Engineering Design Problems.

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	1816093X