This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.

Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB TUO 17 listopadu 2172 15 Ostrava Poruba 708 00 Czech Republic

Faculty of Information Technology University of Finance Marketing Ho Chi Minh City Vietnam

Faculty of Mechanical Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Vietnam

IT4Innovations Quantum Computing Lab VŠB Technical University of Ostrava 17 listopadu 2172 15 Ostrava Poruba 708 00 Czech Republic

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