<p>Multi-Objective Optimization of Soil Tillage Machinery Based on Intelligent Modeling and Simulation</p>

Annotation. A methodology for solving the problem of multicriteria optimization of soilcultivating agricultural machinery (SCAM) during its operation with uncertain priorities according to particular optimality criteria has been developed. The main specific optimality criteria were: the force required to cut the remaining rhizomes and the fuel consumption per hectare of cultivated soil, which were subject to functional limitations. The varied parameters were: the blade installation angle to the bottom of the furrow; the blade installation angle to the furrow wall; the blade sharpening angle; the cutting edge thickness; the unit's movement speed, which were subjected to two-way direct restrictions. Based on the minimax and mid-level convolution, a scalarization of the optimality vector criterion was performed with smooth optimization. At the same time, reserves are maximized, with which functional constraints are satisfied according to particular optimality criteria. After applying the average convolution, a modified criterion was obtained, which made it possible to reduce the multi-criteria optimization problem of SCAM to a standard minimization problem with a scalar criterion. The problem of uncertainty of priorities according to particular optimality criteria is solved by introducing the decision-maker's preference function (DMPF). The approximation of the DMPF is carried out using the fuzzy ANFIS model. The problem of global optimization is solved using a genetic algorithm. The software implementation of the multi-criteria optimization algorithm for SCAM mode parameters was carried out in the MATLAB 2025b environment.