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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Published in:	Cluster computing Vol. 28; no. 9; p. 592
Main Authors:	Zheng, Shixing, Ma, Suyang, Jin, Xu
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2025 Springer Nature B.V
Subjects:	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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Abstract	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.
AbstractList	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.
ArticleNumber	592
Author	Ma, Suyang Jin, Xu Zheng, Shixing
Author_xml	– sequence: 1 givenname: Shixing surname: Zheng fullname: Zheng, Shixing email: b22140221@njupt.edu.cn organization: School of Economics, Nanjing University of Posts and Telecommunications – sequence: 2 givenname: Suyang surname: Ma fullname: Ma, Suyang organization: School of Computer Science & Technology, China University of Mining and Technology – sequence: 3 givenname: Xu surname: Jin fullname: Jin, Xu email: jinxu@njupt.edu.cn organization: School of Science, Nanjing University of Posts and Telecommunications
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SubjectTerms	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
Title	Time series prediction with transformer neural network optimized by IFE and hunger-driven DMOA
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