## 6 Conclusions  
In this paper, we proposed two enhanced versions of TGA and FA swarm intelligence metaheuristics for optimizing the CNN's hyperparameters. The first proposed approach (EE-TGA) enhanced the exploitation ability of the original TGA, while the second proposed metaheuristics (\(E^{3}\)FA) improved both the exploration and exploitation of the basic FA. Based on our previous experience with the TGA method, we came to the conclusion that the exploitation ability, as well as the intensification-diversification balance of this algorithm could be improved in order to establish better solution quality and convergence. We addressed this issue by scattering some of the newly generated solutions in each iteration around the current best solution by using a normal distribution. In this way, the convergence speed and exploitation-exploration balance of the original TGA version were improved. Similarly, founded on our previous experience with the FA metaheuristic, despite the fact that the original FA exhibited relatively stable and efficient exploitation, according to the results of the performed simulations on the standard unconstrained benchmarks, we concluded that the exploitation process of the original FA could be further enhanced and also that the exploitation-exploration balance could be better adjusted. Moreover, the original FA approach in some runs showed behavior of premature convergence because the whole population converged towards the current best solutions. By incorporating two procedures: one that enhanced the exploitation and one that improved exploration, into the original, we addressed both issues. When a modified/improved/hybridized method is created, its effectiveness should first be validated against a wider set of benchmarks, and the performance should be compared with the original method. This is the only way to establish a real improvement of the enhanced method over the original one. For that reason, we firstly performed simulations of the proposed EE-TGA and \(E^{3}\)FA metaheuristics on standard unconstrained benchmarks and compared the generated solutions' quality and convergence with the original algorithms. In most cases, the methods proposed in this paper managed to obtain better results than the original implementations. After conducting tests on standard benchmarks, we adapted and tested the proposed EE-TGA and \(E^{3}\)FA CNNs' hyperparameter optimization. The EE-TGA-CNN and \(E^{3}\)FA-CNN frameworks aimed to optimize the following CNN hyperparameters: the number of convolutional layers, the number of filters, filter size, the number of FC layers, and the the number of hidden units in the FC layers. The proposed frameworks searched for the right set of hyperparameters and automatically designed the CNN architecture for a given task. We did not include the type of pooling layer, activation function, regularization technique, and other CNN hyperparameters in the automatic search because of the limitations of available computing power. The proposed metaheuristics were applied to the well-known MNIST benchmark dataset, and the obtained results were compared to other similar methods that were conducted under the same and similar conditions, as well as to state-of-the-art architectures. The results of both proposed frameworks (EE-TGA-CNN and \(E^{3}\)FA-CNN) were compared to the peer evolutionary competitors that were tested under the same simulation conditions [17], the original implementations of the TGA [71] and FA [72] metaheuristics and with same of the state-of-the-art network configurations that were manually designed. Finally, we included one more comparative analysis with state-of-the-art swarm intelligence approaches [18], which were tested under slightly different experimental conditions. The results proved that the proposed method was robust, achieved high classification accuracy, was competitive in terms of the employed computational resources, efficient in real-world classification tasks, and did not require the user to have any prior expertise in the domain.