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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!