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January 1, 2026International Journal of Ad Hoc and Ubiquitous ComputingOpen Access

A Hybrid Ant Colony Genetic Optimisation Framework for Automated CNN Architecture and Hyperparameter Tuning in Image Classification

Hong Ren Chen Mu-Yen Chen Jia Lang Xu Yi Syuan Wang Po Yen Hsu

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Key Points

To develop a hybrid optimization framework combining ant colony optimization and genetic algorithms for tuning CNN architectures and hyperparameters.
Utilized a hybrid approach integrating ant colony optimization and genetic algorithms.
Applied the framework to optimize Convolutional Neural Network (CNN) architecture for better performance.
Performance evaluated on various image classification tasks.
Achieved significant improvements in classification accuracy compared to standard tuning methods.
Enhanced optimization led to faster convergence rates in finding optimal hyperparameters.
Demonstrated improved model performance across different image datasets.

Implication

Shows enhanced CNN architecture and hyperparameter tuning in image classification, indicating improved accuracy.

Abstract

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a06b95be7dec685947abf0f https://doi.org/10.1504/ijahuc.2026.10078301

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