This article examines the application of artificial intelligence methods to solving problems that arise when searching for solutions to multicriteria models used for analyzing and optimizing processes in various economic sectors. The study applies the Pareto Front Learning (PFL) method and evaluates its effectiveness in identifying Pareto-optimal solutions in multicriteria mathematical models, achieving their rapid and accurate approximation, and addressing resource allocation imbalances in agricultural facility location tasks. The results demonstrate that the practical implementation of this method enables the generation of high-quality solutions that closely approximate the true Pareto frontier.
| Mualliflar | Kutlimuratov, Yusup |
|---|---|
| Jurnal | Techscience.uz - техника фанлари долзарб масалалри |
| Nashr sanasi | 2025-11-30 |
| Jild | 3 |
| Son | 11 |
| Betlar | 24-31 |
| Til | O‘zbek |
| DOI | 10.47390/ts-v3i11y2025no3 |
DOI: 10.47390/ts-v3i11y2025no3 · Maqolaning asl sahifasi
multicriteria models, decision-making, artificial intelligence, Pareto frontier, HyperNetwork, optimal solution., koʻp mezonli modellar, qaror qabul qilish, sun’iy intellekt, Pareto chegarasi, HyperNetwork, optimal yechim.
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