Selecting optimal deep learning models is often a time-consuming process. To address this challenge, we propose a novel variant of the ant colony optimization (ACO) algorithm. This approach is designed to enhance model selection across various deep learning architectures, with a particular focus on leaf classification tasks. We introduce a new ACO technique specifically tailored for selecting robust models within convolutional neural networks (CNNs). These models are then integrated into an ensemble learning framework known as ensemble CNNs. A distinguishing feature of our proposed evolutionary ACO algorithm is its ability to consistently identify a set of robust CNN models in each iteration. This capability is facilitated by an innovative fitness function and an adaptive learning rate schedule embedded within the ACO algorithm, which optimizes pheromone distribution. Unlike the original ACO algorithm, which consistently selects the same CNN model, our evolutionary approach enables the dynamic discovery of new CNN models. To validate our method, we conducted experiments on two plant leaf datasets: Mulberry and Turkey-plant. Our comparison with existing methods, specifically the ant colony system (ACS) and the max-min ant system (MMAS), demonstrated that the MMAS algorithm outperformed the ACS algorithm. Furthermore, we explored three ensemble learning techniques: unweighted average, weighted average, and cost-sensitive learning. The weighted average method emerged as the most effective ensemble approach, with its parameters determined through a grid search process. The results indicate that the evolutionary ACO algorithm not only facilitates the selection of robust deep learning models but also achieves superior performance compared to the original ACO algorithm when applied to the Mulberry leaf and Turkey-plant datasets.