Novel Greylag Goose Optimization Algorithm with Evolutionary Game Theory (EGGO)

In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment...

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Veröffentlicht in:	Biomimetics (Basel, Switzerland) Jg. 10; H. 8; S. 545
Hauptverfasser:	Wang, Lei, Yao, Yuqi, Yang, Yuanting, Zang, Zihao, Zhang, Xinming, Zhang, Yiwen, Yu, Zhenglei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 19.08.2025 MDPI
Schlagworte:	Accuracy Adjustment Algorithms Analysis Anser anser Convergence Cooperation Design optimization Efficiency evolutionary game theory Exploitation Foraging behavior Game theory global search capability greylag goose optimization algorithm Heuristic Mathematical optimization Mutation optimization algorithm robustness Optimization algorithms Ordinary differential equations Swarm intelligence Waterfowl evolutionary game theory global search capability optimization algorithm robustness greylag goose optimization algorithm
ISSN:	2313-7673, 2313-7673
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Abstract	In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment from evolutionary game theory, EGGO improves global search efficiency and convergence speed. Furthermore, EGGO employs dynamic grouping, random mutation, and local search enhancement to boost efficiency and robustness. Experimental comparisons on standard test functions and the CEC 2022 benchmark suite show that EGGO outperforms other classic algorithms and variants in convergence precision and speed. Its effectiveness in practical optimization problems is also demonstrated through applications in engineering design, such as the design of tension/compression springs, gear trains, and three-bar trusses. EGGO offers a novel solution for optimization problems and provides a new theoretical foundation and research framework for swarm intelligence algorithms.
AbstractList	In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment from evolutionary game theory, EGGO improves global search efficiency and convergence speed. Furthermore, EGGO employs dynamic grouping, random mutation, and local search enhancement to boost efficiency and robustness. Experimental comparisons on standard test functions and the CEC 2022 benchmark suite show that EGGO outperforms other classic algorithms and variants in convergence precision and speed. Its effectiveness in practical optimization problems is also demonstrated through applications in engineering design, such as the design of tension/compression springs, gear trains, and three-bar trusses. EGGO offers a novel solution for optimization problems and provides a new theoretical foundation and research framework for swarm intelligence algorithms.In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment from evolutionary game theory, EGGO improves global search efficiency and convergence speed. Furthermore, EGGO employs dynamic grouping, random mutation, and local search enhancement to boost efficiency and robustness. Experimental comparisons on standard test functions and the CEC 2022 benchmark suite show that EGGO outperforms other classic algorithms and variants in convergence precision and speed. Its effectiveness in practical optimization problems is also demonstrated through applications in engineering design, such as the design of tension/compression springs, gear trains, and three-bar trusses. EGGO offers a novel solution for optimization problems and provides a new theoretical foundation and research framework for swarm intelligence algorithms. In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment from evolutionary game theory, EGGO improves global search efficiency and convergence speed. Furthermore, EGGO employs dynamic grouping, random mutation, and local search enhancement to boost efficiency and robustness. Experimental comparisons on standard test functions and the CEC 2022 benchmark suite show that EGGO outperforms other classic algorithms and variants in convergence precision and speed. Its effectiveness in practical optimization problems is also demonstrated through applications in engineering design, such as the design of tension/compression springs, gear trains, and three-bar trusses. EGGO offers a novel solution for optimization problems and provides a new theoretical foundation and research framework for swarm intelligence algorithms.
Audience	Academic
Author	Zhang, Yiwen Wang, Lei Yao, Yuqi Zang, Zihao Zhang, Xinming Yang, Yuanting Yu, Zhenglei
AuthorAffiliation	3 Jilin Provincial Institute of Product Quality Supervision and Inspection, Changchun 130103, China; yangyuanting1984@163.com 1 School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China; wlcust@163.com (L.W.); yaoyq@mails.cust.edu.cn (Y.Y.); zzh9948014@163.com (Z.Z.) 5 College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China; zlyu@jlu.edu.cn 2 School of Mechatronic Engineering and Automation, Foshan University, Foshan 528225, China 4 Automotive Parts Intelligent Manufacturing Assembly Inspection Technology and Equipment University—Enterprise Joint Innovation Laboratory, Changchun University of Science and Technology, Changchun 130022, China
AuthorAffiliation_xml	– name: 3 Jilin Provincial Institute of Product Quality Supervision and Inspection, Changchun 130103, China; yangyuanting1984@163.com – name: 2 School of Mechatronic Engineering and Automation, Foshan University, Foshan 528225, China – name: 4 Automotive Parts Intelligent Manufacturing Assembly Inspection Technology and Equipment University—Enterprise Joint Innovation Laboratory, Changchun University of Science and Technology, Changchun 130022, China – name: 5 College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China; zlyu@jlu.edu.cn – name: 1 School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China; wlcust@163.com (L.W.); yaoyq@mails.cust.edu.cn (Y.Y.); zzh9948014@163.com (Z.Z.)
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SubjectTerms	Accuracy Adjustment Algorithms Analysis Anser anser Convergence Cooperation Design optimization Efficiency evolutionary game theory Exploitation Foraging behavior Game theory global search capability greylag goose optimization algorithm Heuristic Mathematical optimization Mutation optimization algorithm robustness Optimization algorithms Ordinary differential equations Swarm intelligence Waterfowl
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Title	Novel Greylag Goose Optimization Algorithm with Evolutionary Game Theory (EGGO)
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