Wang Lei, Yao Yuqi, Yang Yuanting, Zang Zihao, Zhang Xinming, Zhang Yiwen, Yu Zhenglei
School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
School of Mechatronic Engineering and Automation, Foshan University, Foshan 528225, China.
Biomimetics (Basel). 2025 Aug 19;10(8):545. doi: 10.3390/biomimetics10080545.
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.
本文提出了一种基于进化博弈论的增强灰雁优化算法（EGGO），以解决传统灰雁优化算法（GGO）在全局搜索能力和收敛速度方面的局限性。通过引入进化博弈论中的动态策略调整，EGGO提高了全局搜索效率和收敛速度。此外，EGGO采用动态分组、随机变异和局部搜索增强来提高效率和鲁棒性。在标准测试函数和CEC 2022基准测试集上的实验比较表明，EGGO在收敛精度和速度方面优于其他经典算法和变体。通过在工程设计中的应用，如拉伸/压缩弹簧、齿轮系和三杆桁架的设计，也证明了EGGO在实际优化问题中的有效性。EGGO为优化问题提供了一种新颖的解决方案，并为群体智能算法提供了新的理论基础和研究框架。
