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
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Zusammenfassung: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 convergence mechanism, the algorithm adaptively balances local search and global exploration based on the population’s degree of aggregation. Additionally, a hunger-based regulation strategy is introduced, allowing the algorithm to dynamically adjust the search space, particularly during later iterations, thereby enhancing individual local search capabilities and effectively avoiding local optima. To improve the uniform distribution of the population in the solution space, the Henon chaotic mapping is employed for population initialization. Experimental results on multiple CEC benchmark functions demonstrate that the proposed IFSDMOA algorithm excels in convergence speed, solution accuracy, and robustness. Finally, the IFSDMOA algorithm is applied to the hyperparameter optimization of a Transformer neural network to enhance the performance of a stock market prediction model. Comparative results with other time series prediction models indicate that IFSDMOA-Transformer exhibits superior predictive accuracy and generalization capability in forecasting stock market prices.
Bibliographie:ObjectType-Article-1
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-025-05266-4