Geodesic
Training an artificial neural network
News of the Kabardin-Balkar scientific center of RAS, no. 6 (2023), pp. 95-102

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Particle swarm optimization (PSO) and Jaya are heuristic optimization algorithms that are used to find optimal solutions in optimization problems. Each of these methods has its own advantages and disadvantages, and the choice between them depends on the specific optimization problem and performance requirements. This paper proposes a hybrid of PSO and Jaya algorithms to improve optimization efficiency. In this paper PSO, Jaya, PSOJaya are used as artificial neural network (ANN) training methods for the classification task on the Balance Scale dataset. The results of the hybrid algorithm are compared with the results of the Backpropagation, PSO and Jaya algorithms. The test calculations compare the algorithms based on the mean, median, standard deviation, and "best" minimum error value after 30 simulations. The experiment results show that the ANN trained with PSOJaya has higher accuracy than the one trained with Backpropagation, PSO and Jaya.
Mots-clés : heuristic algorithm, ANN, classification
Keywords: optimization, particle swarm method (PSO), Jaya, Backpropagation, hybrid algorithm, pipeline hybridization 
@article{IZKAB_2023_6_a8,
     author = {E. M. Kazakova},
     title = {Training an artificial neural network},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {95--102},
     publisher = {mathdoc},
     number = {6},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2023_6_a8/}
}
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E. M. Kazakova. Training an artificial neural network. News of the Kabardin-Balkar scientific center of RAS, no. 6 (2023), pp. 95-102. http://geodesic.mathdoc.fr/item/IZKAB_2023_6_a8/