Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification

Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming. Swarm intelligence algorithms have been widely adopted to solve many highly nonlinear, multimodal problems and have succeeded significantly. The Hunger Games Search (HGS) is a recent swarm intelligence algorithm that has shown good performance across various applications. However, the standard HGS still faces limitations, such as restricted population diversity and a tendency to get trapped in local optima, which can hinder its effectiveness. In this paper, we propose an optimized deep learning architecture called EHGS-VGG16 designed based on the VGG16 model and boosted by an enhanced Hunger Games Search (EHGS) algorithm for hyperparameter tuning. The proposed enhancement to HGS involves modified search strategies, incorporating the concepts of "local best" and a "local escaping mechanism" to improve its exploration capability. To validate our approach, the evaluation is conducted in three folds. First, the EHGS algorithm is evaluated through 30 real-valued benchmark functions from the IEEE CEC2014 suite. Second, a custom-developed VGG16 model is tested on the Flickr-27 logo classification dataset and compared against state-of-the-art deep learning models such as ResNet50V2, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2. Finally, EHGS is integrated into the VGG16 model to optimize its hyperparameters. The experimental results show that VGG16 outperformed the other counterparts with an accuracy of 0.956966, a precision of 0.957137, and a recall of 0.956966. Moreover, the integration of EHGS further improved classification quality by 3%. These findings highlight the potential of combining evolutionary optimization techniques with deep learning for enhanced accuracy in log classification tasks.