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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Zusammenfassung: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.
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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics10080545