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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Vydané v:	Biomimetics (Basel, Switzerland) Ročník 10; číslo 8; s. 545
Hlavní autori:	Wang, Lei, Yao, Yuqi, Yang, Yuanting, Zang, Zihao, Zhang, Xinming, Zhang, Yiwen, Yu, Zhenglei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 19.08.2025 MDPI
Predmet:	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
On-line prístup:	Získať plný text
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