Optimizing Convolutional Neural Networks: A Comprehensive Review of Hyperparameter Tuning Through Metaheuristic Algorithms

MQ Ibrahim and NK Hussein and D Guinovart and M Qaraad, ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 32, 5123-5160 (2025).

DOI: 10.1007/s11831-025-10292-x

Convolutional neural networks have become essential in computer vision, especially for image classification. They depend heavily on hyperparameters, and there is no practical way to manually tune these numerous settings through trial and error. This made it necessary for automated methods, especially those that come with metaheuristic algorithms, to optimize the hyperparameters and build good network architectures. Metaheuristic algorithms provide an easy way of determining the best hyperparameters by generating and testing various combinations using intuitive strategies and principles of solution- finding. This review provides a comprehensive discussion of convolutional neural networks, such as their layers, architectural designs, types, and ways of improvement, with a focus on optimization using metaheuristic algorithms. It highlights prominent algorithms and recent studies aimed at improving hyperparameter selection. By combining results of current and future research, this review should be a helpful resource for researchers, serving as the basis for further research and innovation in automated hyperparameter optimization using metaheuristic approaches, contributing significantly to further development in this field. The study concludes that metaheuristic algorithms significantly enhance the performance of convolutional neural networks with a simple yet effective replacement for manual tuning and high future prospects for automated optimization breakthroughs.

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