## 2 Problem formulation and related literature review
### Metaheuristics applications for CNN optimization  
Based on the literature survey, it can be seen that both types of metaheuristics, evolutionary algorithms and swarm intelligence, have many applications and implementations in the domain of CNNs and computer vision. These methods were validated against various benchmark and practical datasets. For example, in [73], an approach that combined GA and CNNs was proposed for non-invasive classification of glioma using magnetic resonance imaging (MRI). The structure of the deep network was evolved by using GA, and in this way, a typical selection of neural network for a particular problem based on trial and error was avoided. The proposed method was validated against two case studies. In the first case study, 90.9% classification accuracy for three glioma gradeswas obtained. In the second study, glioma, meningioma, and pituitary tumor types were classified with 94.2% accuracy. A framework for automating the CNN design by using EAs implemented in the Python programming environment was shown in [74]. The framework utilized two EAs: GA and grammatical evolution. It was proven that the proposed framework managed to obtain better results than many state-of-the-art CNN architectures.  
Another interesting method was presented in [20], where the authors proposed the gradient-priority PSO (GPSO) with gradient penalties for selecting the CNN structure. The proposed method was tested by using three types of emotion states for each subject and simultaneously collecting EEG signals corresponding to each emotion category. Experimental results proved that the GPSO managed to obtain competitive classification performance over the dataset. An orthogonal learning particle swarm optimization (OLPSO) algorithm that optimized hyperparameters' values for VGG16 and VGG19 CNNs that have been developed for the task of plant disease diagnosis by classifying the leaf images as healthy and unhealthy was presented in [14]. By performing practical simulations, the authors proved that their approach managed to obtain higher performance than other methods that were tested for the same datasets. Another state-of-the-art method was presented in [75]. The developed method fell into the group of hybrid stochastic training algorithms. The authors employed the relatively novel grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks (GOAMLP). The GOAMLP model was applied to five practical datasets: Parkinson's, diabetes, breast cancer, coronary heart disease, and orthopedic patients.  
In [13], a novel variant of the PSO algorithm, named cPSO-CNN, was proposed for determining the CNN architecture. The cPSO-CNN method enhanced the canonical PSO's exploration ability and redefined PSO's scalar acceleration coefficients as vectors to better adapt to the variant ranges of CNN hyperparameters. It was proven that the cPSO-CNN established competitive performance in terms of accuracy and overall computation costs when compared to seven other state-of-the-art methods. One more PSO-based approach used for defining CNN architecture that is worth mentioning was presented in [19]. By using their PSO adaptation, the authors managed to obtain better parameter settings than those found by the AlexNet configuration for five different image datasets.