551.509.313.14 Comparative analysis of methods for converting optimality criteria in multi-criteria optimization problems

Tlibekov A. T. (Bauman Moscow State Technical University)

CONVERTING OPTIMALITY CRITERIA, INTERPOLATION, FRACTIONAL-POWER SERIES, MULTI-CRITERIA OPTIMIZATION, GENETIC ALGORITHM, INFINITE-STEP OPTIMIZATION METHODS


doi: 10.18698/2309-3684-2024-2-112125


The comparison of existing and developed new methods of converting optimality criteria into a scalar function of the goal is performed. New converting methods are used in the problems of interpolation of experimental data by a modified fractional-power Newton–Puiseux series. Coefficients and degrees of a fractional-power series are calculated by evolutionary or infinite-step optimization methods, where the modules of the difference between experimental data and the values obtained by calculating the interpolation polynomial are used as optimality criteria. Under such conditions, the optimization task becomes multi-criteria, for which, during the search process, part of the optimality criteria increases, the rest decrease and reduce the scalar goal function and creating the illusion that the search is effective. For new converting methods, all optimality criteria in the search process are reduced. The errors obtained by interpolating the time of laser cutting of steel sheet and forecasting the production program of parts are shown. The use of modified fractional power series and new methods of converting optimality criteria for the implementation of the neural network learning function is proposed.


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