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

Lei Wang, Yuqi Yao, Yuanting Yang, Zihao Zang, Xinming Zhang, Yiwen Zhang, Zhenglei Yu

Abstract read

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Article in Biomimetics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

High-Precision and Efficient Calibration of Robot Polishing Systems Using an Adaptive Residual EKF Optimized by MIPO.Sensors (Basel, Switzerland) · 2026
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7 authors.

Lei WangSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Yuqi YaoSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0009-0004-3514-2129

Yuanting YangJilin Provincial Institute of Product Quality Supervision and Inspection, Changchun 130103, China.

Zihao ZangSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0009-0008-6266-2153

Xinming ZhangSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0000-0002-6713-1430

Yiwen ZhangSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0009-0005-7387-917X

Zhenglei YuCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.

Funding

the Jilin Province and Changchun City Major Science and Technology Special 20240301008ZD

6 · The paper itself

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.

Indexed as

evolutionary game theoryglobal search capabilitygreylag goose optimization algorithmoptimization algorithm robustness

Identifiers

PMID40862917

DOI10.3390/biomimetics10080545

PMCPMC12383611

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