Time series prediction with transformer neural network optimized by IFE and hunger-driven DMOA

This study proposes an enhanced Dwarf Mongoose Optimization Algorithm(DMOA) that integrates an Intuitionistic Fuzzy Entropy Perturbation Convergence Factor and a Hunger-driven Search Strategy. By incorporating the population’s Intuitionistic Fuzzy Entropy (IFE) as a perturbation factor into the conv...

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Veröffentlicht in:	Cluster computing Jg. 28; H. 9; S. 592
Hauptverfasser:	Zheng, Shixing, Ma, Suyang, Jin, Xu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial intelligence Bibliometrics Computer Communication Networks Computer engineering Computer Science Convergence Deep learning Design engineering Energy consumption Entropy Foraging behavior Genetic algorithms Mathematical functions Mathematical models Neural networks Operating Systems Optimization Optimization algorithms Optimization techniques Perturbation Prediction models Processor Architectures Search methods Solution space Time series Traveling salesman problem Volleyball Time series prediction Transformer Dwarf mongoose optimization algorithm (DMOA) Intuitionistic fuzzy entropy (IFE) Hunger-driven strategy
ISSN:	1386-7857, 1573-7543
Online-Zugang:	Volltext
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