Title | Using neural network approximation of the objective function in global optimization problems |
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Authors | S. N. Karpenko^{1}, I. G. Lebedev^{1}, D. V. Nadumin^{1}^{1}N. I. Lobachevsky State University of Nizhny Novgorod |

Annotation | The paper considers the problem of finding the global minimum of a multiextremal function. To solve this problem, the simulation annealing algorithm was used. To speed up the process of finding the minimum, the approximation of the objective function by a neural network is applied. The results of algorithm that combines the simulation annealing algorithm using a neural network to approximate objective function and the Nelder-Meade method to refine the solution of the optimization problem are presented. The paper presentS the results of the experiments carried out with a series of multiextremal test problems. The results show a reduction in the number of calculations of the objective function while maintaining high search accuracy. |

Keywords | optimization methods, global optimization, neural networks, machine learning. |

Citation | Karpenko S. N., Lebedev I. G., Nadumin D. V. ''Using neural network approximation of the objective function in global optimization problems'' [Electronic resource]. Proceedings of the International Scientific Youth School-Seminar "Mathematical Modeling, Numerical Methods and Software complexes" named after E.V. Voskresensky (Saransk, July 14-18, 2022). Saransk: SVMO Publ, 2022. - pp. 111-119. Available at: https://conf.svmo.ru/files/2022/papers/paper17.pdf. - Date of access: 25.04.2024. |

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