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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Published in:	Biomimetics (Basel, Switzerland) Vol. 10; no. 8; p. 545
Main Authors:	Wang, Lei, Yao, Yuqi, Yang, Yuanting, Zang, Zihao, Zhang, Xinming, Zhang, Yiwen, Yu, Zhenglei
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 19.08.2025 MDPI
Subjects:	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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Cites_doi	10.1016/j.asoc.2012.11.026 10.1007/s10489-020-02081-9 10.1007/s11831-022-09800-0 10.3390/biomimetics9070417 10.1007/s00366-022-01638-1 10.3389/fenrg.2024.1401330 10.1162/106454699568728 10.1016/j.asoc.2019.105744 10.1016/j.knosys.2015.07.006 10.1016/0025-5564(78)90077-9 10.1038/s41598-025-00796-8 10.1016/j.eswa.2023.122147 10.1016/j.ins.2009.03.004 10.1002/nme.1620210904 10.1016/j.advengsoft.2017.01.004 10.1016/j.asoc.2015.10.048 10.1016/j.asoc.2018.08.028 10.1038/s41598-025-99472-0 10.1007/s40747-022-00794-7 10.1016/j.future.2019.02.028 10.1155/2022/5191758 10.1080/21642583.2019.1708830 10.1016/j.advengsoft.2013.12.007 10.1016/j.advengsoft.2016.01.008 10.1007/s00366-011-0241-y 10.3390/a14040122 10.1515/mt-2024-0516 10.3390/math13030373 10.3390/biomimetics9080478 10.1007/s12065-024-01011-9 10.1016/j.engappai.2019.08.025 10.1016/j.advengsoft.2015.01.010 10.1016/j.asoc.2020.106086 10.3390/biomimetics8020235
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References	Chu (ref_13) 2023; 9 Khodadadi (ref_10) 2024; 238 Heidari (ref_24) 2019; 97 Mirjalili (ref_7) 2014; 69 Taylor (ref_21) 1978; 40 Elhosseini (ref_26) 2018; 73 ref_11 Wu (ref_25) 2022; 2022 Dorigo (ref_8) 1999; 5 Mirjalili (ref_22) 2015; 89 Sun (ref_14) 2019; 85 ref_17 Gandomi (ref_29) 2013; 29 ref_16 ref_15 Mirjalili (ref_27) 2015; 83 Liu (ref_12) 2023; 39 Rashedi (ref_31) 2009; 179 Mashru (ref_18) 2025; 18 Belegundu (ref_34) 1985; 21 Sadollah (ref_30) 2013; 13 Xue (ref_23) 2020; 8 Casella (ref_4) 2024; 11 Mehta (ref_19) 2025; 67 Ma (ref_33) 2021; 51 ref_20 Saremi (ref_28) 2017; 105 ref_1 ref_3 Guedria (ref_32) 2016; 40 ref_2 Bardsiri (ref_35) 2019; 86 Xu (ref_36) 2020; 89 ref_5 Mirjalili (ref_9) 2016; 95 Mohammadi (ref_6) 2023; 30
References_xml	– volume: 13 start-page: 2592 year: 2013 ident: ref_30 article-title: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2012.11.026 – volume: 51 start-page: 1 year: 2021 ident: ref_33 article-title: Moth-flame optimization algorithm based on diversity and mutation strategy publication-title: Appl. Intell. doi: 10.1007/s10489-020-02081-9 – volume: 30 start-page: 331 year: 2023 ident: ref_6 article-title: Nature-Inspired Metaheuristic Search Algorithms for Optimizing BenchmarkProblems: Inclined Planes System Optimization to State-of-the-Art Methods publication-title: Arch. Computat. Methods Eng. doi: 10.1007/s11831-022-09800-0 – ident: ref_2 doi: 10.3390/biomimetics9070417 – volume: 39 start-page: 2433 year: 2023 ident: ref_12 article-title: A novel enhanced global exploration whale optimization algorithm based on Lévy flights and judgment mechanism for global continuous optimization problems publication-title: Eng. Comput. doi: 10.1007/s00366-022-01638-1 – ident: ref_16 doi: 10.3389/fenrg.2024.1401330 – ident: ref_3 – volume: 5 start-page: 137 year: 1999 ident: ref_8 article-title: Ant Algorithms for Discrete Optimization publication-title: Artif. Life doi: 10.1162/106454699568728 – volume: 85 start-page: 105744 year: 2019 ident: ref_14 article-title: A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105744 – volume: 89 start-page: 228 year: 2015 ident: ref_22 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2015.07.006 – volume: 40 start-page: 145 year: 1978 ident: ref_21 article-title: Evolutionary stable strategies and game dynamics publication-title: Math. Biosci. doi: 10.1016/0025-5564(78)90077-9 – ident: ref_15 doi: 10.1038/s41598-025-00796-8 – volume: 238 start-page: 122147 year: 2024 ident: ref_10 article-title: Greylag Goose Optimization: Nature-inspired optimization algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.122147 – volume: 179 start-page: 2232 year: 2009 ident: ref_31 article-title: GSA: A Gravitational Search Algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2009.03.004 – volume: 21 start-page: 1583 year: 1985 ident: ref_34 article-title: A study of mathematical programming methods for structural optimization. Part I: Theory publication-title: Int. J. Numer. Methods Eng. doi: 10.1002/nme.1620210904 – volume: 105 start-page: 30 year: 2017 ident: ref_28 article-title: Grasshopper Optimisation Algorithm: Theory and application publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2017.01.004 – volume: 40 start-page: 455 year: 2016 ident: ref_32 article-title: Improved accelerated PSO algorithm for mechanical engineering optimization problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.10.048 – volume: 11 start-page: 2381828 year: 2024 ident: ref_4 article-title: A modified binary bat algorithm for machine loading in flexible manufacturing systems: A case study publication-title: Int. J. Syst. Sci. Oper. Logist. – volume: 73 start-page: 24 year: 2018 ident: ref_26 article-title: A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2018.08.028 – ident: ref_17 doi: 10.1038/s41598-025-99472-0 – volume: 9 start-page: 213 year: 2023 ident: ref_13 article-title: Architecture entropy sampling-based evolutionary neural architecture search and its application in osteoporosis diagnosis publication-title: Complex Intell. Syst. doi: 10.1007/s40747-022-00794-7 – volume: 97 start-page: 849 year: 2019 ident: ref_24 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.02.028 – volume: 2022 start-page: 5191758 year: 2022 ident: ref_25 article-title: Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot publication-title: Comput. Intell. Neurosci. doi: 10.1155/2022/5191758 – volume: 8 start-page: 22 year: 2020 ident: ref_23 article-title: A novel swarm intelligence optimization approach: Sparrow search algorithm publication-title: Syst. Sci. Control. Eng. doi: 10.1080/21642583.2019.1708830 – volume: 69 start-page: 46 year: 2014 ident: ref_7 article-title: Grey Wolf Optimizer publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.12.007 – volume: 95 start-page: 51 year: 2016 ident: ref_9 article-title: The Whale Optimization Algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 29 start-page: 17 year: 2013 ident: ref_29 article-title: Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems publication-title: Eng. Comput. doi: 10.1007/s00366-011-0241-y – ident: ref_5 doi: 10.3390/a14040122 – volume: 67 start-page: 900 year: 2025 ident: ref_19 article-title: Enhanced Greylag Goose optimizer for solving constrained engineering design problems publication-title: Mater. Test. doi: 10.1515/mt-2024-0516 – ident: ref_20 doi: 10.3390/math13030373 – ident: ref_1 doi: 10.3390/biomimetics9080478 – volume: 18 start-page: 25 year: 2025 ident: ref_18 article-title: Reliability-based multi-objective optimization of trusses with greylag goose algorithm publication-title: Evol. Intell. doi: 10.1007/s12065-024-01011-9 – volume: 86 start-page: 165 year: 2019 ident: ref_35 article-title: Poor and rich optimization algorithm: A new human-based and multi populations algorithm publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2019.08.025 – volume: 83 start-page: 80 year: 2015 ident: ref_27 article-title: The Ant Lion Optimizer publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2015.01.010 – volume: 89 start-page: 106086 year: 2020 ident: ref_36 article-title: Multivariable grey prediction evolution algorithm: A new metaheuristic publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2020.106086 – ident: ref_11 doi: 10.3390/biomimetics8020235
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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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